Descritiva - Bloco 2
2022-07-06
Separating Data
db_blocos <- db %>%
select("rh_age_read_br", "rh_read_b4_yng_br", "rh_read_since_yng_br", "rh_read_b4_old_br",
"rh_read_with_b4_old_br", "rh_readin_b4_old_br", "rh_read_since_old_br",
"rh_read_w_since_old_br", "rh_readin_since_old_br", "rh_read_time_b4_br",
"rh_read_time_since_br",
"hlr_books_b4_corrected",
"hlr_dbooks_b4_corrected",
"hlr_games_b4_corrected",
"hlr_dgames_b4_corrected",
"hlr_ad_books_b4_corrected",
"hlr_ad_dbooks_b4_corrected",
"hlr_ad_news_b4_corrected",
"hlr_books_since_corrected",
"hlr_dbooks_since_corrected",
"hlr_games_since_corrected",
"hlr_dgames_since_corrected",
"hlr_ad_books_since_corrected",
"hlr_ad_dbooks_since_corrected",
"hlr_ad_news_since_corrected",
"ea_read_b4_ALL_br",
"ea_indr_b4_ALL_br",
"ea_letter_b4_yng_br",
"ea_tv_b4_yng_br",
"ea_boardg_b4_yng_br",
"ea_vidg_b4_yng_br",
"ea_edapp_b4_yng_br",
"ea_read_since_yng_br",
"ea_indr_since_yng_br",
"ea_letter_since_yng_br",
"ea_tv_since_yng_br",
"ea_boardg_since_yng_br",
"ea_vidg_since_yng_br",
"ea_edapp_since_yng_br")
db_out_na <- db_blocos[rowSums(is.na(db_blocos)) < 1, ]
db_bloco_3 <- db_out_na %>%
dplyr::select(
"ea_read_b4_ALL_br",
"ea_indr_b4_ALL_br",
"ea_letter_b4_yng_br",
"ea_tv_b4_yng_br",
"ea_boardg_b4_yng_br",
"ea_vidg_b4_yng_br",
"ea_edapp_b4_yng_br",
"ea_read_since_yng_br",
"ea_indr_since_yng_br",
"ea_letter_since_yng_br",
"ea_tv_since_yng_br",
"ea_boardg_since_yng_br",
"ea_vidg_since_yng_br",
"ea_edapp_since_yng_br"
)
db_bloco_3_pre <- db_bloco_3 %>%
select(
"ea_read_b4_ALL_br",
"ea_indr_b4_ALL_br",
"ea_letter_b4_yng_br",
"ea_tv_b4_yng_br",
"ea_boardg_b4_yng_br",
"ea_vidg_b4_yng_br",
"ea_edapp_b4_yng_br"
)
db_bloco_3_pos <- db_bloco_3 %>%
select(
"ea_read_since_yng_br",
"ea_indr_since_yng_br",
"ea_letter_since_yng_br",
"ea_tv_since_yng_br",
"ea_boardg_since_yng_br",
"ea_vidg_since_yng_br",
"ea_edapp_since_yng_br"
)Analise Fatorial
Matriz de correlação entre as variáveis do bloco 3
matcorr_3 <- cor(db_bloco_3)
print(matcorr_3, digits = 2)## ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br
## ea_read_b4_ALL_br 1.00 0.40 0.39
## ea_indr_b4_ALL_br 0.40 1.00 0.39
## ea_letter_b4_yng_br 0.39 0.39 1.00
## ea_tv_b4_yng_br 0.31 0.37 0.48
## ea_boardg_b4_yng_br 0.30 0.43 0.61
## ea_vidg_b4_yng_br 0.22 0.32 0.39
## ea_edapp_b4_yng_br 0.21 0.23 0.37
## ea_read_since_yng_br 0.72 0.31 0.42
## ea_indr_since_yng_br 0.38 0.71 0.36
## ea_letter_since_yng_br 0.33 0.32 0.73
## ea_tv_since_yng_br 0.30 0.31 0.39
## ea_boardg_since_yng_br 0.26 0.33 0.57
## ea_vidg_since_yng_br 0.23 0.30 0.30
## ea_edapp_since_yng_br 0.21 0.23 0.29
## ea_tv_b4_yng_br ea_boardg_b4_yng_br ea_vidg_b4_yng_br
## ea_read_b4_ALL_br 0.31 0.30 0.22
## ea_indr_b4_ALL_br 0.37 0.43 0.32
## ea_letter_b4_yng_br 0.48 0.61 0.39
## ea_tv_b4_yng_br 1.00 0.42 0.68
## ea_boardg_b4_yng_br 0.42 1.00 0.42
## ea_vidg_b4_yng_br 0.68 0.42 1.00
## ea_edapp_b4_yng_br 0.61 0.34 0.66
## ea_read_since_yng_br 0.31 0.32 0.24
## ea_indr_since_yng_br 0.29 0.34 0.21
## ea_letter_since_yng_br 0.42 0.54 0.35
## ea_tv_since_yng_br 0.66 0.37 0.54
## ea_boardg_since_yng_br 0.34 0.62 0.33
## ea_vidg_since_yng_br 0.51 0.29 0.61
## ea_edapp_since_yng_br 0.47 0.32 0.47
## ea_edapp_b4_yng_br ea_read_since_yng_br
## ea_read_b4_ALL_br 0.21 0.72
## ea_indr_b4_ALL_br 0.23 0.31
## ea_letter_b4_yng_br 0.37 0.42
## ea_tv_b4_yng_br 0.61 0.31
## ea_boardg_b4_yng_br 0.34 0.32
## ea_vidg_b4_yng_br 0.66 0.24
## ea_edapp_b4_yng_br 1.00 0.25
## ea_read_since_yng_br 0.25 1.00
## ea_indr_since_yng_br 0.18 0.49
## ea_letter_since_yng_br 0.33 0.48
## ea_tv_since_yng_br 0.49 0.40
## ea_boardg_since_yng_br 0.29 0.39
## ea_vidg_since_yng_br 0.49 0.29
## ea_edapp_since_yng_br 0.68 0.31
## ea_indr_since_yng_br ea_letter_since_yng_br
## ea_read_b4_ALL_br 0.38 0.33
## ea_indr_b4_ALL_br 0.71 0.32
## ea_letter_b4_yng_br 0.36 0.73
## ea_tv_b4_yng_br 0.29 0.42
## ea_boardg_b4_yng_br 0.34 0.54
## ea_vidg_b4_yng_br 0.21 0.35
## ea_edapp_b4_yng_br 0.18 0.33
## ea_read_since_yng_br 0.49 0.48
## ea_indr_since_yng_br 1.00 0.44
## ea_letter_since_yng_br 0.44 1.00
## ea_tv_since_yng_br 0.34 0.49
## ea_boardg_since_yng_br 0.45 0.68
## ea_vidg_since_yng_br 0.30 0.35
## ea_edapp_since_yng_br 0.28 0.37
## ea_tv_since_yng_br ea_boardg_since_yng_br
## ea_read_b4_ALL_br 0.30 0.26
## ea_indr_b4_ALL_br 0.31 0.33
## ea_letter_b4_yng_br 0.39 0.57
## ea_tv_b4_yng_br 0.66 0.34
## ea_boardg_b4_yng_br 0.37 0.62
## ea_vidg_b4_yng_br 0.54 0.33
## ea_edapp_b4_yng_br 0.49 0.29
## ea_read_since_yng_br 0.40 0.39
## ea_indr_since_yng_br 0.34 0.45
## ea_letter_since_yng_br 0.49 0.68
## ea_tv_since_yng_br 1.00 0.46
## ea_boardg_since_yng_br 0.46 1.00
## ea_vidg_since_yng_br 0.70 0.38
## ea_edapp_since_yng_br 0.62 0.40
## ea_vidg_since_yng_br ea_edapp_since_yng_br
## ea_read_b4_ALL_br 0.23 0.21
## ea_indr_b4_ALL_br 0.30 0.23
## ea_letter_b4_yng_br 0.30 0.29
## ea_tv_b4_yng_br 0.51 0.47
## ea_boardg_b4_yng_br 0.29 0.32
## ea_vidg_b4_yng_br 0.61 0.47
## ea_edapp_b4_yng_br 0.49 0.68
## ea_read_since_yng_br 0.29 0.31
## ea_indr_since_yng_br 0.30 0.28
## ea_letter_since_yng_br 0.35 0.37
## ea_tv_since_yng_br 0.70 0.62
## ea_boardg_since_yng_br 0.38 0.40
## ea_vidg_since_yng_br 1.00 0.67
## ea_edapp_since_yng_br 0.67 1.00Mapa de calor para a correlação entre as variáveis do bloco 3
Pode-se observar que as variáveis que apresentam maior correlação são justamente aquelas variáveis pré e pós pandemais, tal com ea_read_b4_AAL_br e ea_read_since_yng_br que indicam a frequeência com a que a criança se envolvia em alguma atividade de leitura.
corrplot(matcorr_3, method="circle")
Matriz de correlação entre as variáveis do bloco 3 pré-pandemia
matcorr_pre_3 <- cor(db_bloco_3_pre)
print(matcorr_pre_3, digits = 2)## ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br
## ea_read_b4_ALL_br 1.00 0.40 0.39
## ea_indr_b4_ALL_br 0.40 1.00 0.39
## ea_letter_b4_yng_br 0.39 0.39 1.00
## ea_tv_b4_yng_br 0.31 0.37 0.48
## ea_boardg_b4_yng_br 0.30 0.43 0.61
## ea_vidg_b4_yng_br 0.22 0.32 0.39
## ea_edapp_b4_yng_br 0.21 0.23 0.37
## ea_tv_b4_yng_br ea_boardg_b4_yng_br ea_vidg_b4_yng_br
## ea_read_b4_ALL_br 0.31 0.30 0.22
## ea_indr_b4_ALL_br 0.37 0.43 0.32
## ea_letter_b4_yng_br 0.48 0.61 0.39
## ea_tv_b4_yng_br 1.00 0.42 0.68
## ea_boardg_b4_yng_br 0.42 1.00 0.42
## ea_vidg_b4_yng_br 0.68 0.42 1.00
## ea_edapp_b4_yng_br 0.61 0.34 0.66
## ea_edapp_b4_yng_br
## ea_read_b4_ALL_br 0.21
## ea_indr_b4_ALL_br 0.23
## ea_letter_b4_yng_br 0.37
## ea_tv_b4_yng_br 0.61
## ea_boardg_b4_yng_br 0.34
## ea_vidg_b4_yng_br 0.66
## ea_edapp_b4_yng_br 1.00Mapa de calor para a correlação entre as variáveis do bloco 3 pré-pandemia
Pode-se observar que as variávies ea_read_b4_AAL_br e ea_edapp_b4_yng_br possuem a menor correlação, ou seja, crianças que se envolvem em alguma atividade de leitura e crianças que utilizam algum aplicativo educacional em tablet são pouco correlacionadas, enquanto que as variáveis ea_vidg_b4_yng_br e ea_read_b4_AAL_br têm a maior correlação, ou seja, crianças que se envolvem em alguma atividade de leitura são fortemente correlacionadas com crianças que assitem vídeos ou jogam jogos educacionais no computador.
corrplot(matcorr_pre_3, method="circle")
Matriz de correlação entre as variáveis do bloco 3 pós-pandemia
matcorr_pos_3 <- cor(db_bloco_3_pos)
print(matcorr_pos_3, digits = 2)## ea_read_since_yng_br ea_indr_since_yng_br
## ea_read_since_yng_br 1.00 0.49
## ea_indr_since_yng_br 0.49 1.00
## ea_letter_since_yng_br 0.48 0.44
## ea_tv_since_yng_br 0.40 0.34
## ea_boardg_since_yng_br 0.39 0.45
## ea_vidg_since_yng_br 0.29 0.30
## ea_edapp_since_yng_br 0.31 0.28
## ea_letter_since_yng_br ea_tv_since_yng_br
## ea_read_since_yng_br 0.48 0.40
## ea_indr_since_yng_br 0.44 0.34
## ea_letter_since_yng_br 1.00 0.49
## ea_tv_since_yng_br 0.49 1.00
## ea_boardg_since_yng_br 0.68 0.46
## ea_vidg_since_yng_br 0.35 0.70
## ea_edapp_since_yng_br 0.37 0.62
## ea_boardg_since_yng_br ea_vidg_since_yng_br
## ea_read_since_yng_br 0.39 0.29
## ea_indr_since_yng_br 0.45 0.30
## ea_letter_since_yng_br 0.68 0.35
## ea_tv_since_yng_br 0.46 0.70
## ea_boardg_since_yng_br 1.00 0.38
## ea_vidg_since_yng_br 0.38 1.00
## ea_edapp_since_yng_br 0.40 0.67
## ea_edapp_since_yng_br
## ea_read_since_yng_br 0.31
## ea_indr_since_yng_br 0.28
## ea_letter_since_yng_br 0.37
## ea_tv_since_yng_br 0.62
## ea_boardg_since_yng_br 0.40
## ea_vidg_since_yng_br 0.67
## ea_edapp_since_yng_br 1.00Mapa de calor para a correlação entre as variáveis do bloco 3 pós-pandemia
Pode-se observar que a menor correlação continua sendo entre as variáveis ea_read_b4_AAL_br e ea_edapp_b4_yng_br, enquanto que a maior correlação passar a ser entre as variáveis ea_tv_since_yng_br e ea_vidg_since_yng_br, ou seja, entre crianças que assistem a vídeos/programas educacionais com computador e na TV.
corrplot(matcorr_pos_3, method="circle")
Multiple Correspondence Analysis
PRÉ-PANDEMIA
db_bloco_3_pref <- as.data.frame(lapply(db_bloco_3_pre, as.factor))summary(db_bloco_3_pref)## ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br ea_tv_b4_yng_br
## 0:113 0:114 0:198 0:121
## 1:181 1:163 1:188 1:137
## 2:106 2:115 2:113 2:126
## 3: 51 3: 64 3: 36 3: 55
## 4:114 4:109 4: 57 4:111
## 5: 39 5: 34 5: 18 5: 42
## 6: 11 6: 16 6: 5 6: 23
## ea_boardg_b4_yng_br ea_vidg_b4_yng_br ea_edapp_b4_yng_br
## 0:158 0:115 0:156
## 1:208 1:150 1:132
## 2:135 2:127 2:110
## 3: 55 3: 60 3: 69
## 4: 32 4: 97 4: 87
## 5: 16 5: 39 5: 32
## 6: 11 6: 27 6: 29Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.
for (i in 1:7) {
plot(db_bloco_3_pref[,i], main=colnames(db_bloco_3_pref)[i],
ylab = "Count", col="steelblue", las = 2)
}
MCA com pacote FactoMineR
MCA(db_bloco_3_pref, ncp = 5, graph = TRUE) ## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"res.mca_pref_3 <- MCA(db_bloco_3_pref, graph = FALSE)
print(res.mca_pref_3)## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"Visualização e interpretação
Pode-se observar que 51% da variância é explicada pelas primeiras 10 dimensões
eig.val_pref_3 <- get_eigenvalue(res.mca_pref_3)
eig.val_pref_3## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 0.53965386 8.9942310 8.994231
## Dim.2 0.43300683 7.2167804 16.211011
## Dim.3 0.38417548 6.4029247 22.613936
## Dim.4 0.36257435 6.0429058 28.656842
## Dim.5 0.33287255 5.5478759 34.204718
## Dim.6 0.25225400 4.2042334 38.408951
## Dim.7 0.22067876 3.6779793 42.086930
## Dim.8 0.18603825 3.1006375 45.187568
## Dim.9 0.17535371 2.9225618 48.110130
## Dim.10 0.17415158 2.9025264 51.012656
## Dim.11 0.16450366 2.7417277 53.754384
## Dim.12 0.15453608 2.5756013 56.329985
## Dim.13 0.14931659 2.4886098 58.818595
## Dim.14 0.14184741 2.3641235 61.182718
## Dim.15 0.13603103 2.2671838 63.449902
## Dim.16 0.13033998 2.1723330 65.622235
## Dim.17 0.12630414 2.1050690 67.727304
## Dim.18 0.12051967 2.0086612 69.735965
## Dim.19 0.11845507 1.9742511 71.710216
## Dim.20 0.11530086 1.9216811 73.631898
## Dim.21 0.11212570 1.8687616 75.500659
## Dim.22 0.10839202 1.8065336 77.307193
## Dim.23 0.10647306 1.7745510 79.081744
## Dim.24 0.09834111 1.6390185 80.720762
## Dim.25 0.09721966 1.6203276 82.341090
## Dim.26 0.09502890 1.5838151 83.924905
## Dim.27 0.09108665 1.5181108 85.443016
## Dim.28 0.08801978 1.4669963 86.910012
## Dim.29 0.08201853 1.3669755 88.276988
## Dim.30 0.07843515 1.3072525 89.584240
## Dim.31 0.07216892 1.2028153 90.787055
## Dim.32 0.07053652 1.1756086 91.962664
## Dim.33 0.06937332 1.1562220 93.118886
## Dim.34 0.06211855 1.0353092 94.154195
## Dim.35 0.06102216 1.0170360 95.171231
## Dim.36 0.05183080 0.8638467 96.035078
## Dim.37 0.05091034 0.8485056 96.883584
## Dim.38 0.04960845 0.8268075 97.710391
## Dim.39 0.04085766 0.6809611 98.391352
## Dim.40 0.03505673 0.5842788 98.975631
## Dim.41 0.03231867 0.5386445 99.514275
## Dim.42 0.02914347 0.4857245 100.000000Representação gráfica da porcentagem explicada por cada dimensão
fviz_screeplot(res.mca_pref_3, addlabels = TRUE, ylim = c(0, 10))
Biplot de indivíduos e categorias de variáveis:
Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.
fviz_mca_biplot(res.mca_pref_3,
repel = TRUE, # Avoid text overlapping (slow if many point)
ggtheme = theme_minimal())## Warning: ggrepel: 532 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
#Gráfico de variáveis #Resultados Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:
var_pref_3 <- get_mca_var(res.mca_pref_3)
var_pref_3## Multiple Correspondence Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for categories"
## 2 "$cos2" "Cos2 for categories"
## 3 "$contrib" "contributions of categories"# Coordinates
var_pref_3$coord## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 -0.365576095 0.5221916 0.532895429 -0.481676016
## ea_read_b4_ALL_br_1 -0.328143613 0.2851807 -0.256338476 0.163790113
## ea_read_b4_ALL_br_2 -0.226260687 -0.4935147 -0.320572505 0.421249292
## ea_read_b4_ALL_br_3 -0.124136091 -0.4496996 -0.313451599 0.287104599
## ea_read_b4_ALL_br_4 0.369503882 -0.6549571 0.413918765 -0.348789576
## ea_read_b4_ALL_br_5 1.993024610 0.5854658 -0.579641711 -0.375317566
## ea_read_b4_ALL_br_6 1.015205808 1.4958027 1.051462295 1.808001506
## ea_indr_b4_ALL_br_0 -0.371243515 0.5747139 0.747631394 -0.617634955
## ea_indr_b4_ALL_br_1 -0.518267187 0.3385811 -0.459077438 0.261336258
## ea_indr_b4_ALL_br_2 -0.247909241 -0.4027810 -0.226148337 0.380782753
## ea_indr_b4_ALL_br_3 0.134273909 -0.6735214 -0.187389077 0.199150971
## ea_indr_b4_ALL_br_4 0.575818652 -0.6464343 0.347329537 -0.473985781
## ea_indr_b4_ALL_br_5 1.936485777 0.6666111 -0.711466136 0.023761758
## ea_indr_b4_ALL_br_6 1.131912209 1.0322277 0.870658262 1.383340404
## ea_letter_b4_yng_br_0 -0.395504573 0.3554691 0.437156725 -0.496542171
## ea_letter_b4_yng_br_1 -0.378972736 0.2532449 -0.373414405 0.196356039
## ea_letter_b4_yng_br_2 -0.004989159 -0.5855608 -0.297402447 0.626528161
## ea_letter_b4_yng_br_3 0.421848357 -1.0160964 -0.223724017 0.007325075
## ea_letter_b4_yng_br_4 1.077173243 -0.9656051 0.700464767 -0.401430691
## ea_letter_b4_yng_br_5 3.524956845 1.9487305 -1.290736852 -0.089321603
## ea_letter_b4_yng_br_6 2.017183161 0.9434529 1.722437867 2.965673572
## ea_tv_b4_yng_br_0 -0.590258403 0.6009218 0.592189473 -0.768691332
## ea_tv_b4_yng_br_1 -0.636850661 0.5379464 -0.509270133 0.093803774
## ea_tv_b4_yng_br_2 -0.309305268 -0.5316640 -0.456419843 0.545564047
## ea_tv_b4_yng_br_3 0.025139995 -0.7488060 -0.105804944 0.655548537
## ea_tv_b4_yng_br_4 0.649574395 -0.9668301 0.576081258 -0.450736210
## ea_tv_b4_yng_br_5 2.301910599 0.9268541 -1.278739396 -0.632645480
## ea_tv_b4_yng_br_6 1.194633402 1.3110461 2.226316910 2.259440180
## ea_boardg_b4_yng_br_0 -0.307128326 0.5597988 0.627588031 -0.351394252
## ea_boardg_b4_yng_br_1 -0.385695313 0.2118604 -0.389384401 -0.047585075
## ea_boardg_b4_yng_br_2 -0.103979726 -0.5189409 -0.352573620 0.460101029
## ea_boardg_b4_yng_br_3 0.584827066 -1.0221739 0.067075476 -0.047860244
## ea_boardg_b4_yng_br_4 0.918271506 -1.5009540 0.976433862 -0.353292632
## ea_boardg_b4_yng_br_5 3.589976186 1.5882341 -1.969301342 -1.164541394
## ea_boardg_b4_yng_br_6 2.163487993 1.4891075 2.364024712 3.261335314
## ea_vidg_b4_yng_br_0 -0.544985110 0.5597291 0.592744767 -0.832223747
## ea_vidg_b4_yng_br_1 -0.623846661 0.5027695 -0.515470759 0.127388271
## ea_vidg_b4_yng_br_2 -0.290110478 -0.5534331 -0.460178432 0.568482271
## ea_vidg_b4_yng_br_3 0.252129365 -0.6362411 -0.002691924 0.634073827
## ea_vidg_b4_yng_br_4 0.576966008 -1.0483415 0.642645585 -0.653294772
## ea_vidg_b4_yng_br_5 2.243485345 0.7541302 -1.486543357 -0.885057994
## ea_vidg_b4_yng_br_6 1.277960164 1.5168234 2.348063372 2.379358033
## ea_edapp_b4_yng_br_0 -0.345347260 0.4118886 0.356905068 -0.583357363
## ea_edapp_b4_yng_br_1 -0.616777602 0.4640648 -0.450047773 0.026300709
## ea_edapp_b4_yng_br_2 -0.313581062 -0.4133528 -0.591427791 0.597683813
## ea_edapp_b4_yng_br_3 0.068119640 -0.6240383 0.054334256 0.622372635
## ea_edapp_b4_yng_br_4 0.590140228 -1.2551249 0.647761519 -0.621980263
## ea_edapp_b4_yng_br_5 2.237227325 0.9097543 -1.386862499 -0.922782303
## ea_edapp_b4_yng_br_6 1.453413915 1.4862068 1.829705398 2.154635874
## Dim 5
## ea_read_b4_ALL_br_0 0.36875268
## ea_read_b4_ALL_br_1 -0.56629968
## ea_read_b4_ALL_br_2 0.43817218
## ea_read_b4_ALL_br_3 0.47854146
## ea_read_b4_ALL_br_4 -0.14389993
## ea_read_b4_ALL_br_5 0.28299791
## ea_read_b4_ALL_br_6 -0.42300023
## ea_indr_b4_ALL_br_0 0.47166329
## ea_indr_b4_ALL_br_1 -0.64531617
## ea_indr_b4_ALL_br_2 0.52005743
## ea_indr_b4_ALL_br_3 0.43672385
## ea_indr_b4_ALL_br_4 -0.50958478
## ea_indr_b4_ALL_br_5 0.41873650
## ea_indr_b4_ALL_br_6 0.31048064
## ea_letter_b4_yng_br_0 0.39251595
## ea_letter_b4_yng_br_1 -0.66838755
## ea_letter_b4_yng_br_2 0.61285072
## ea_letter_b4_yng_br_3 0.56876092
## ea_letter_b4_yng_br_4 -0.90791934
## ea_letter_b4_yng_br_5 0.67985157
## ea_letter_b4_yng_br_6 -0.45494971
## ea_tv_b4_yng_br_0 0.64879881
## ea_tv_b4_yng_br_1 -0.85759874
## ea_tv_b4_yng_br_2 0.58421451
## ea_tv_b4_yng_br_3 0.61751319
## ea_tv_b4_yng_br_4 -0.67736415
## ea_tv_b4_yng_br_5 0.24768148
## ea_tv_b4_yng_br_6 -0.16535147
## ea_boardg_b4_yng_br_0 0.34407342
## ea_boardg_b4_yng_br_1 -0.45201964
## ea_boardg_b4_yng_br_2 0.47634505
## ea_boardg_b4_yng_br_3 -0.02020594
## ea_boardg_b4_yng_br_4 -0.84112765
## ea_boardg_b4_yng_br_5 0.56781783
## ea_boardg_b4_yng_br_6 -0.51888807
## ea_vidg_b4_yng_br_0 0.70537334
## ea_vidg_b4_yng_br_1 -0.80675027
## ea_vidg_b4_yng_br_2 0.61161799
## ea_vidg_b4_yng_br_3 0.54415619
## ea_vidg_b4_yng_br_4 -0.76587303
## ea_vidg_b4_yng_br_5 0.29215759
## ea_vidg_b4_yng_br_6 -0.27906338
## ea_edapp_b4_yng_br_0 0.52403307
## ea_edapp_b4_yng_br_1 -0.82941540
## ea_edapp_b4_yng_br_2 0.66002090
## ea_edapp_b4_yng_br_3 0.38393279
## ea_edapp_b4_yng_br_4 -0.74894682
## ea_edapp_b4_yng_br_5 0.21629521
## ea_edapp_b4_yng_br_6 -0.45251936var_pref_3$cos2## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 3.008363e-02 0.06138107 6.392323e-02 5.222576e-02
## ea_read_b4_ALL_br_1 4.490728e-02 0.03391791 2.740416e-02 1.118830e-02
## ea_read_b4_ALL_br_2 1.066120e-02 0.05072105 2.140132e-02 3.695443e-02
## ea_read_b4_ALL_br_3 1.393437e-03 0.01828673 8.884481e-03 7.453691e-03
## ea_read_b4_ALL_br_4 3.106742e-02 0.09760967 3.898498e-02 2.768179e-02
## ea_read_b4_ALL_br_5 2.689475e-01 0.02320840 2.274895e-02 9.537618e-03
## ea_read_b4_ALL_br_6 1.876999e-02 0.04074782 2.013461e-02 5.953239e-02
## ea_indr_b4_ALL_br_0 3.136064e-02 0.07515719 1.271868e-01 8.680223e-02
## ea_indr_b4_ALL_br_1 9.686271e-02 0.04134039 7.600131e-02 2.462910e-02
## ea_indr_b4_ALL_br_2 1.413557e-02 0.03731348 1.176291e-02 3.334897e-02
## ea_indr_b4_ALL_br_3 2.094169e-03 0.05269037 4.078655e-03 4.606735e-03
## ea_indr_b4_ALL_br_4 7.142454e-02 0.09001705 2.598720e-02 4.839568e-02
## ea_indr_b4_ALL_br_5 2.194479e-01 0.02600446 2.962179e-02 3.304151e-05
## ea_indr_b4_ALL_br_6 3.422305e-02 0.02846061 2.024830e-02 5.111534e-02
## ea_letter_b4_yng_br_0 7.427320e-02 0.05999745 9.074098e-02 1.170689e-01
## ea_letter_b4_yng_br_1 6.323331e-02 0.02823653 6.139205e-02 1.697534e-02
## ea_letter_b4_yng_br_2 5.603113e-06 0.07718247 1.990966e-02 8.836004e-02
## ea_letter_b4_yng_br_3 1.106462e-02 0.06419390 3.112069e-03 3.336170e-06
## ea_letter_b4_yng_br_4 1.185255e-01 0.09524446 5.012025e-02 1.646121e-02
## ea_letter_b4_yng_br_5 3.746328e-01 0.11449902 5.023120e-02 2.405532e-04
## ea_letter_b4_yng_br_6 3.335269e-02 0.00729593 2.431797e-02 7.209197e-02
## ea_tv_b4_yng_br_0 8.533806e-02 0.08844929 8.589735e-02 1.447313e-01
## ea_tv_b4_yng_br_1 1.162433e-01 0.08294127 7.433427e-02 2.521932e-03
## ea_tv_b4_yng_br_2 2.465110e-02 0.07283434 5.367731e-02 7.669255e-02
## ea_tv_b4_yng_br_3 6.207333e-05 0.05506977 1.099478e-03 4.220699e-02
## ea_tv_b4_yng_br_4 9.292878e-02 0.20586985 7.309033e-02 4.474426e-02
## ea_tv_b4_yng_br_5 3.883932e-01 0.06296764 1.198557e-01 2.933699e-02
## ea_tv_b4_yng_br_6 5.544667e-02 0.06677933 1.925662e-01 1.983389e-01
## ea_boardg_b4_yng_br_0 3.261224e-02 0.10834400 1.361727e-01 4.269040e-02
## ea_boardg_b4_yng_br_1 7.602521e-02 0.02293869 7.748650e-02 1.157205e-03
## ea_boardg_b4_yng_br_2 3.040814e-03 0.07574052 3.496167e-02 5.953864e-02
## ea_boardg_b4_yng_br_3 3.359151e-02 0.10261816 4.418778e-04 2.249699e-04
## ea_boardg_b4_yng_br_4 4.628323e-02 0.12365628 5.233197e-02 6.850947e-03
## ea_boardg_b4_yng_br_5 3.442519e-01 0.06737863 1.035899e-01 3.622455e-02
## ea_boardg_b4_yng_br_6 8.524418e-02 0.04038386 1.017794e-01 1.937076e-01
## ea_vidg_b4_yng_br_0 6.831202e-02 0.07205825 8.080966e-02 1.592972e-01
## ea_vidg_b4_yng_br_1 1.255434e-01 0.08154103 8.571294e-02 5.234765e-03
## ea_vidg_b4_yng_br_2 2.190336e-02 0.07971025 5.511076e-02 8.410421e-02
## ea_vidg_b4_yng_br_3 6.872348e-03 0.04376246 7.834007e-07 4.346482e-02
## ea_vidg_b4_yng_br_4 6.233650e-02 0.20580101 7.733659e-02 7.992090e-02
## ea_vidg_b4_yng_br_5 3.407914e-01 0.03850657 1.496226e-01 5.303781e-02
## ea_vidg_b4_yng_br_6 7.499306e-02 0.10564684 2.531664e-01 2.599597e-01
## ea_edapp_b4_yng_br_0 4.053442e-02 0.05765957 4.329297e-02 1.156595e-01
## ea_edapp_b4_yng_br_1 1.039642e-01 0.05885510 5.535337e-02 1.890435e-04
## ea_edapp_b4_yng_br_2 2.141909e-02 0.03721714 7.619119e-02 7.781159e-02
## ea_edapp_b4_yng_br_3 5.864097e-04 0.04921289 3.730817e-04 4.895053e-02
## ea_edapp_b4_yng_br_4 5.738465e-02 0.25957281 6.913781e-02 6.374389e-02
## ea_edapp_b4_yng_br_5 2.747272e-01 0.04542863 1.055719e-01 4.673906e-02
## ea_edapp_b4_yng_br_6 1.045392e-01 0.10930975 1.656772e-01 2.297461e-01
## Dim 5
## ea_read_b4_ALL_br_0 3.060871e-02
## ea_read_b4_ALL_br_1 1.337462e-01
## ea_read_b4_ALL_br_2 3.998321e-02
## ea_read_b4_ALL_br_3 2.070762e-02
## ea_read_b4_ALL_br_4 4.711816e-03
## ea_read_b4_ALL_br_5 5.422613e-03
## ea_read_b4_ALL_br_6 3.258644e-03
## ea_indr_b4_ALL_br_0 5.062106e-02
## ea_indr_b4_ALL_br_1 1.501738e-01
## ea_indr_b4_ALL_br_2 6.220574e-02
## ea_indr_b4_ALL_br_3 2.215349e-02
## ea_indr_b4_ALL_br_4 5.593825e-02
## ea_indr_b4_ALL_br_5 1.026088e-02
## ea_indr_b4_ALL_br_6 2.574911e-03
## ea_letter_b4_yng_br_0 7.315496e-02
## ea_letter_b4_yng_br_1 1.966920e-01
## ea_letter_b4_yng_br_2 8.454426e-02
## ea_letter_b4_yng_br_3 2.011330e-02
## ea_letter_b4_yng_br_4 8.420448e-02
## ea_letter_b4_yng_br_5 1.393562e-02
## ea_letter_b4_yng_br_6 1.696551e-03
## ea_tv_b4_yng_br_0 1.031047e-01
## ea_tv_b4_yng_br_1 2.107953e-01
## ea_tv_b4_yng_br_2 8.794403e-02
## ea_tv_b4_yng_br_3 3.745132e-02
## ea_tv_b4_yng_br_4 1.010501e-01
## ea_tv_b4_yng_br_5 4.496574e-03
## ea_tv_b4_yng_br_6 1.062239e-03
## ea_boardg_b4_yng_br_0 4.093013e-02
## ea_boardg_b4_yng_br_1 1.044200e-01
## ea_boardg_b4_yng_br_2 6.381692e-02
## ea_boardg_b4_yng_br_3 4.009893e-05
## ea_boardg_b4_yng_br_4 3.883338e-02
## ea_boardg_b4_yng_br_5 8.612143e-03
## ea_boardg_b4_yng_br_6 4.903465e-03
## ea_vidg_b4_yng_br_0 1.144369e-01
## ea_vidg_b4_yng_br_1 2.099503e-01
## ea_vidg_b4_yng_br_2 9.735189e-02
## ea_vidg_b4_yng_br_3 3.201145e-02
## ea_vidg_b4_yng_br_4 1.098387e-01
## ea_vidg_b4_yng_br_5 5.779317e-03
## ea_vidg_b4_yng_br_6 3.575956e-03
## ea_edapp_b4_yng_br_0 9.333173e-02
## ea_edapp_b4_yng_br_1 1.880057e-01
## ea_edapp_b4_yng_br_2 9.488918e-02
## ea_edapp_b4_yng_br_3 1.862803e-02
## ea_edapp_b4_yng_br_4 9.242454e-02
## ea_edapp_b4_yng_br_5 2.567883e-03
## ea_edapp_b4_yng_br_6 1.013386e-02var_pref_3$contrib## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 0.6500482318 1.6529903 1.940258687 1.6796479133
## ea_read_b4_ALL_br_1 0.8389151644 0.7896795 0.719123322 0.3110883208
## ea_read_b4_ALL_br_2 0.2335799855 1.3849629 0.658651384 1.2050747869
## ea_read_b4_ALL_br_3 0.0338281200 0.5532830 0.302976091 0.2693272167
## ea_read_b4_ALL_br_4 0.6699685569 2.6233888 1.180952105 0.8885085506
## ea_read_b4_ALL_br_5 6.6680905377 0.7171330 0.792284335 0.3519590670
## ea_read_b4_ALL_br_6 0.4879916949 1.3203024 0.735321553 2.3036687352
## ea_indr_b4_ALL_br_0 0.6762918612 2.0199487 3.852805752 2.7861105994
## ea_indr_b4_ALL_br_1 1.8845453365 1.0024094 2.077096648 0.7132088242
## ea_indr_b4_ALL_br_2 0.3042249509 1.0008474 0.355616448 1.0682727425
## ea_indr_b4_ALL_br_3 0.0496677854 1.5574530 0.135883187 0.1626202393
## ea_indr_b4_ALL_br_4 1.5556414714 2.4434720 0.795073238 1.5688697621
## ea_indr_b4_ALL_br_5 5.4880631275 0.8105058 1.040601833 0.0012298888
## ea_indr_b4_ALL_br_6 0.8823827898 0.9145409 0.733352262 1.9615883485
## ea_letter_b4_yng_br_0 1.3331523026 1.3421496 2.287898325 3.1275714112
## ea_letter_b4_yng_br_1 1.1622119611 0.6468020 1.585028909 0.4643828167
## ea_letter_b4_yng_br_2 0.0001210723 2.0785215 0.604316873 2.8417731282
## ea_letter_b4_yng_br_3 0.2757571509 1.9939049 0.108949419 0.0001237532
## ea_letter_b4_yng_br_4 2.8468037377 2.8510579 1.691004057 0.5884719533
## ea_letter_b4_yng_br_5 9.6270155171 3.6669770 1.813197565 0.0092005817
## ea_letter_b4_yng_br_6 0.8757340390 0.2387496 0.896921297 2.8173867361
## ea_tv_b4_yng_br_0 1.8146015798 2.3439773 2.565688570 4.5805655753
## ea_tv_b4_yng_br_1 2.3917034645 2.1268198 2.148395249 0.0772308752
## ea_tv_b4_yng_br_2 0.5188678190 1.9106324 1.587072556 2.4026571179
## ea_tv_b4_yng_br_3 0.0014962515 1.6543729 0.037228181 1.5142659674
## ea_tv_b4_yng_br_4 2.0160083742 5.5661560 2.227345940 1.4447664367
## ea_tv_b4_yng_br_5 9.5793877713 1.9355489 4.152509010 1.0769607516
## ea_tv_b4_yng_br_6 1.4128911365 2.1207813 6.892852875 7.5224498022
## ea_boardg_b4_yng_br_0 0.6415173265 2.6561534 3.762734664 1.2499025649
## ea_boardg_b4_yng_br_1 1.3318754582 0.5008355 1.906854853 0.0301741206
## ea_boardg_b4_yng_br_2 0.0628264708 1.9503009 1.014683503 1.8309224256
## ea_boardg_b4_yng_br_3 0.8097093879 3.0827928 0.014961923 0.0080712773
## ea_boardg_b4_yng_br_4 1.1614586517 3.8673799 1.844730149 0.2558874029
## ea_boardg_b4_yng_br_5 8.8759465055 2.1651156 3.751816068 1.3901434257
## ea_boardg_b4_yng_br_6 2.2162217965 1.3085094 3.717011563 7.4957200547
## ea_vidg_b4_yng_br_0 1.4702076271 1.9327950 2.443039567 5.1027914039
## ea_vidg_b4_yng_br_1 2.5128034955 2.0340483 2.409886174 0.1559480374
## ea_vidg_b4_yng_br_2 0.4600892744 2.0867291 1.626123188 2.6294646573
## ea_vidg_b4_yng_br_3 0.1641759994 1.3029472 0.000026289 1.5454703294
## ea_vidg_b4_yng_br_4 1.3899006154 5.7188587 2.422211117 2.6522830337
## ea_vidg_b4_yng_br_5 8.4493371306 1.1898423 5.210950775 1.9572118327
## ea_vidg_b4_yng_br_6 1.8980600798 3.3324675 9.000789286 9.7929407240
## ea_edapp_b4_yng_br_0 0.8008444770 1.4197641 1.201509560 3.4011339277
## ea_edapp_b4_yng_br_1 2.1614374805 1.5249770 1.616547117 0.0058497677
## ea_edapp_b4_yng_br_2 0.4655902708 1.0082460 2.326447969 2.5174765863
## ea_edapp_b4_yng_br_3 0.0137817806 1.4414634 0.012316683 1.7122999198
## ea_edapp_b4_yng_br_4 1.3041911543 7.3523338 2.207225700 2.1562654619
## ea_edapp_b4_yng_br_5 6.8941664788 1.4207913 3.721465444 1.7457347602
## ea_edapp_b4_yng_br_6 2.6368667473 3.4362805 5.870262735 8.6253263843
## Dim 5
## ea_read_b4_ALL_br_0 1.072253972
## ea_read_b4_ALL_br_1 4.050606540
## ea_read_b4_ALL_br_2 1.420183022
## ea_read_b4_ALL_br_3 0.815001073
## ea_read_b4_ALL_br_4 0.164730831
## ea_read_b4_ALL_br_5 0.217961566
## ea_read_b4_ALL_br_6 0.137348128
## ea_indr_b4_ALL_br_0 1.769774203
## ea_indr_b4_ALL_br_1 4.736761976
## ea_indr_b4_ALL_br_2 2.170447596
## ea_indr_b4_ALL_br_3 0.851809828
## ea_indr_b4_ALL_br_4 1.975187120
## ea_indr_b4_ALL_br_5 0.416015412
## ea_indr_b4_ALL_br_6 0.107631130
## ea_letter_b4_yng_br_0 2.128769615
## ea_letter_b4_yng_br_1 5.860887879
## ea_letter_b4_yng_br_2 2.961670294
## ea_letter_b4_yng_br_3 0.812663677
## ea_letter_b4_yng_br_4 3.278825103
## ea_letter_b4_yng_br_5 0.580563294
## ea_letter_b4_yng_br_6 0.072218033
## ea_tv_b4_yng_br_0 3.554301959
## ea_tv_b4_yng_br_1 7.031333507
## ea_tv_b4_yng_br_2 3.000986765
## ea_tv_b4_yng_br_3 1.463538093
## ea_tv_b4_yng_br_4 3.553990404
## ea_tv_b4_yng_br_5 0.179798151
## ea_tv_b4_yng_br_6 0.043882648
## ea_boardg_b4_yng_br_0 1.305293522
## ea_boardg_b4_yng_br_1 2.965697219
## ea_boardg_b4_yng_br_2 2.137597504
## ea_boardg_b4_yng_br_3 0.001567003
## ea_boardg_b4_yng_br_4 1.579874756
## ea_boardg_b4_yng_br_5 0.359987064
## ea_boardg_b4_yng_br_6 0.206675460
## ea_vidg_b4_yng_br_0 3.992866391
## ea_vidg_b4_yng_br_1 6.812685721
## ea_vidg_b4_yng_br_2 3.315225548
## ea_vidg_b4_yng_br_3 1.239787494
## ea_vidg_b4_yng_br_4 3.970398949
## ea_vidg_b4_yng_br_5 0.232299257
## ea_vidg_b4_yng_br_6 0.146729804
## ea_edapp_b4_yng_br_0 2.989446896
## ea_edapp_b4_yng_br_1 6.336755854
## ea_edapp_b4_yng_br_2 3.343928025
## ea_edapp_b4_yng_br_3 0.709754845
## ea_edapp_b4_yng_br_4 3.405415248
## ea_edapp_b4_yng_br_5 0.104470252
## ea_edapp_b4_yng_br_6 0.414401369#Correlação entre variáveis e dimensões principais
Pode-se observar que para as duas principais dimensões, a maioria das variáveis têm um correlação alta, destoando as variáveis ea_read_b4_AAL_br e ea_indr_b4_AAL_br.
fviz_mca_var(res.mca_pref_3, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
head(round(var_pref_3$coord, 2), 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 -0.37 0.52 0.53 -0.48 0.37
## ea_read_b4_ALL_br_1 -0.33 0.29 -0.26 0.16 -0.57
## ea_read_b4_ALL_br_2 -0.23 -0.49 -0.32 0.42 0.44
## ea_read_b4_ALL_br_3 -0.12 -0.45 -0.31 0.29 0.48Agora visualizaando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionário, é possível um grupo de alternativas que são bem explicadas pelas principais dimensões e um grupo de alternativas que não são explicadas pelas principais dimensões.
fviz_mca_var(res.mca_pref_3,
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
head(var_pref_3$cos2, 10)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 0.030083635 0.06138107 0.063923231 0.052225760 0.030608714
## ea_read_b4_ALL_br_1 0.044907281 0.03391791 0.027404157 0.011188303 0.133746206
## ea_read_b4_ALL_br_2 0.010661205 0.05072105 0.021401323 0.036954425 0.039983212
## ea_read_b4_ALL_br_3 0.001393437 0.01828673 0.008884481 0.007453691 0.020707622
## ea_read_b4_ALL_br_4 0.031067416 0.09760967 0.038984984 0.027681787 0.004711816
## ea_read_b4_ALL_br_5 0.268947460 0.02320840 0.022748951 0.009537618 0.005422613
## ea_read_b4_ALL_br_6 0.018769985 0.04074782 0.020134607 0.059532391 0.003258644
## ea_indr_b4_ALL_br_0 0.031360637 0.07515719 0.127186842 0.086802225 0.050621065
## ea_indr_b4_ALL_br_1 0.096862706 0.04134039 0.076001308 0.024629098 0.150173834
## ea_indr_b4_ALL_br_2 0.014135568 0.03731348 0.011762906 0.033348966 0.062205737Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados, o que indica que essas alternativas fazem parte daquele grupo de alternativas no campo inferior esquerdo do gráfico acima.
fviz_mca_var(res.mca_pref_3, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_cos2(res.mca_pref_3, choice = "var", axes = 1:2)
head(round(var_pref_3$contrib,2), 49)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 0.65 1.65 1.94 1.68 1.07
## ea_read_b4_ALL_br_1 0.84 0.79 0.72 0.31 4.05
## ea_read_b4_ALL_br_2 0.23 1.38 0.66 1.21 1.42
## ea_read_b4_ALL_br_3 0.03 0.55 0.30 0.27 0.82
## ea_read_b4_ALL_br_4 0.67 2.62 1.18 0.89 0.16
## ea_read_b4_ALL_br_5 6.67 0.72 0.79 0.35 0.22
## ea_read_b4_ALL_br_6 0.49 1.32 0.74 2.30 0.14
## ea_indr_b4_ALL_br_0 0.68 2.02 3.85 2.79 1.77
## ea_indr_b4_ALL_br_1 1.88 1.00 2.08 0.71 4.74
## ea_indr_b4_ALL_br_2 0.30 1.00 0.36 1.07 2.17
## ea_indr_b4_ALL_br_3 0.05 1.56 0.14 0.16 0.85
## ea_indr_b4_ALL_br_4 1.56 2.44 0.80 1.57 1.98
## ea_indr_b4_ALL_br_5 5.49 0.81 1.04 0.00 0.42
## ea_indr_b4_ALL_br_6 0.88 0.91 0.73 1.96 0.11
## ea_letter_b4_yng_br_0 1.33 1.34 2.29 3.13 2.13
## ea_letter_b4_yng_br_1 1.16 0.65 1.59 0.46 5.86
## ea_letter_b4_yng_br_2 0.00 2.08 0.60 2.84 2.96
## ea_letter_b4_yng_br_3 0.28 1.99 0.11 0.00 0.81
## ea_letter_b4_yng_br_4 2.85 2.85 1.69 0.59 3.28
## ea_letter_b4_yng_br_5 9.63 3.67 1.81 0.01 0.58
## ea_letter_b4_yng_br_6 0.88 0.24 0.90 2.82 0.07
## ea_tv_b4_yng_br_0 1.81 2.34 2.57 4.58 3.55
## ea_tv_b4_yng_br_1 2.39 2.13 2.15 0.08 7.03
## ea_tv_b4_yng_br_2 0.52 1.91 1.59 2.40 3.00
## ea_tv_b4_yng_br_3 0.00 1.65 0.04 1.51 1.46
## ea_tv_b4_yng_br_4 2.02 5.57 2.23 1.44 3.55
## ea_tv_b4_yng_br_5 9.58 1.94 4.15 1.08 0.18
## ea_tv_b4_yng_br_6 1.41 2.12 6.89 7.52 0.04
## ea_boardg_b4_yng_br_0 0.64 2.66 3.76 1.25 1.31
## ea_boardg_b4_yng_br_1 1.33 0.50 1.91 0.03 2.97
## ea_boardg_b4_yng_br_2 0.06 1.95 1.01 1.83 2.14
## ea_boardg_b4_yng_br_3 0.81 3.08 0.01 0.01 0.00
## ea_boardg_b4_yng_br_4 1.16 3.87 1.84 0.26 1.58
## ea_boardg_b4_yng_br_5 8.88 2.17 3.75 1.39 0.36
## ea_boardg_b4_yng_br_6 2.22 1.31 3.72 7.50 0.21
## ea_vidg_b4_yng_br_0 1.47 1.93 2.44 5.10 3.99
## ea_vidg_b4_yng_br_1 2.51 2.03 2.41 0.16 6.81
## ea_vidg_b4_yng_br_2 0.46 2.09 1.63 2.63 3.32
## ea_vidg_b4_yng_br_3 0.16 1.30 0.00 1.55 1.24
## ea_vidg_b4_yng_br_4 1.39 5.72 2.42 2.65 3.97
## ea_vidg_b4_yng_br_5 8.45 1.19 5.21 1.96 0.23
## ea_vidg_b4_yng_br_6 1.90 3.33 9.00 9.79 0.15
## ea_edapp_b4_yng_br_0 0.80 1.42 1.20 3.40 2.99
## ea_edapp_b4_yng_br_1 2.16 1.52 1.62 0.01 6.34
## ea_edapp_b4_yng_br_2 0.47 1.01 2.33 2.52 3.34
## ea_edapp_b4_yng_br_3 0.01 1.44 0.01 1.71 0.71
## ea_edapp_b4_yng_br_4 1.30 7.35 2.21 2.16 3.41
## ea_edapp_b4_yng_br_5 6.89 1.42 3.72 1.75 0.10
## ea_edapp_b4_yng_br_6 2.64 3.44 5.87 8.63 0.4115 principais categorias de variáveis que contribuem para cada uma das principais dimensões:
# Contributions of rows to dimension 1
fviz_contrib(res.mca_pref_3, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.mca_pref_3, choice = "var", axes = 2, top = 15)
15 principais categorias de variáveis que contribuem as duas principais dimensões combinadas:
fviz_contrib(res.mca_pref_3, choice = "var", axes = 1:2, top = 15)
fviz_mca_var(res.mca_pref_3, alpha.var="contrib",
repel = TRUE,
ggtheme = theme_minimal())## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
##Gráfico de indivíduos Resultados
lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:
ind_pref_3 <- get_mca_ind(res.mca_pref_3)
ind_pref_3## Multiple Correspondence Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"colorir os indivíduos por seus valores de cos2:
fviz_mca_ind(res.mca_pref_3, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping (slow if many points)
ggtheme = theme_minimal())## Warning: ggrepel: 534 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Cos2 of individuals
fviz_cos2(res.mca_pref_3, choice = "ind", axes = 1:2, top = 20)
# Contribution of individuals to the dimensions
fviz_contrib(res.mca_pref_3, choice = "ind", axes = 1:2, top = 20)
POS-PANDEMIA
db_bloco_3_posf <- as.data.frame(lapply(db_bloco_3_pos, as.factor))summary(db_bloco_3_posf)## ea_read_since_yng_br ea_indr_since_yng_br ea_letter_since_yng_br
## 0:153 0:121 0:206
## 1:156 1:165 1:157
## 2: 91 2: 86 2: 98
## 3: 64 3: 65 3: 55
## 4: 98 4:112 4: 71
## 5: 32 5: 36 5: 14
## 6: 21 6: 30 6: 14
## ea_tv_since_yng_br ea_boardg_since_yng_br ea_vidg_since_yng_br
## 0: 90 0:126 0: 70
## 1:121 1:188 1:115
## 2: 97 2:144 2: 99
## 3: 77 3: 65 3: 73
## 4:140 4: 56 4:141
## 5: 43 5: 20 5: 59
## 6: 47 6: 16 6: 58
## ea_edapp_since_yng_br
## 0:147
## 1: 99
## 2: 76
## 3: 62
## 4:126
## 5: 53
## 6: 52Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.
for (i in 1:7) {
plot(db_bloco_3_posf[,i], main=colnames(db_bloco_3_posf)[i],
ylab = "Count", col="steelblue", las = 2)
}
MCA(db_bloco_3_posf,ncp = 5, graph = TRUE) ## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"res.mca_posf_3 <- MCA(db_bloco_3_posf, graph = FALSE)
print(res.mca_posf_3)## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"#Visualização e interpretação Autovalores/variâncias retidos por cada dimensão (eixo). Pode-se observar que 54% da variância é explicada pelas primeiras 10 dimensões
eig.val_posf_3 <- get_eigenvalue(res.mca_posf_3)
eig.val_posf_3## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 0.55091374 9.1818956 9.181896
## Dim.2 0.49805913 8.3009855 17.482881
## Dim.3 0.42119891 7.0199818 24.502863
## Dim.4 0.41994189 6.9990314 31.501894
## Dim.5 0.35137285 5.8562142 37.358109
## Dim.6 0.28992657 4.8321095 42.190218
## Dim.7 0.21342736 3.5571226 45.747341
## Dim.8 0.18172303 3.0287172 48.776058
## Dim.9 0.17345913 2.8909854 51.667043
## Dim.10 0.15594025 2.5990041 54.266047
## Dim.11 0.15179133 2.5298554 56.795903
## Dim.12 0.14572539 2.4287565 59.224659
## Dim.13 0.14155815 2.3593026 61.583962
## Dim.14 0.13939213 2.3232021 63.907164
## Dim.15 0.12599827 2.0999712 66.007135
## Dim.16 0.12455524 2.0759206 68.083056
## Dim.17 0.12170844 2.0284740 70.111530
## Dim.18 0.12041429 2.0069048 72.118435
## Dim.19 0.11042161 1.8403601 73.958795
## Dim.20 0.10876704 1.8127841 75.771579
## Dim.21 0.10400710 1.7334516 77.505031
## Dim.22 0.09767326 1.6278877 79.132918
## Dim.23 0.09552075 1.5920125 80.724931
## Dim.24 0.08985304 1.4975507 82.222481
## Dim.25 0.08808931 1.4681552 83.690637
## Dim.26 0.08535544 1.4225906 85.113227
## Dim.27 0.08080926 1.3468210 86.460048
## Dim.28 0.08049425 1.3415708 87.801619
## Dim.29 0.07538253 1.2563755 89.057995
## Dim.30 0.07223514 1.2039190 90.261914
## Dim.31 0.07108740 1.1847900 91.446704
## Dim.32 0.06640850 1.1068084 92.553512
## Dim.33 0.06266182 1.0443636 93.597875
## Dim.34 0.05621992 0.9369986 94.534874
## Dim.35 0.05405585 0.9009309 95.435805
## Dim.36 0.04958513 0.8264188 96.262224
## Dim.37 0.04723205 0.7872008 97.049425
## Dim.38 0.04258798 0.7097997 97.759224
## Dim.39 0.04031902 0.6719836 98.431208
## Dim.40 0.03436495 0.5727492 99.003957
## Dim.41 0.03121637 0.5202728 99.524230
## Dim.42 0.02854620 0.4757700 100.000000Porcentagens de inércia explicadas por cada dimensão MCA
fviz_screeplot(res.mca_posf_3, addlabels = TRUE, ylim = c(0, 10))
Biplot de indivíduos e categorias de variáveis:
Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.
fviz_mca_biplot(res.mca_posf_3,
repel = TRUE, # Avoid text overlapping (slow if many point)
ggtheme = theme_minimal())## Warning: ggrepel: 526 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
#Gráfico de variáveis #Resultados
Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:
var_posf_3 <- get_mca_var(res.mca_posf_3)
var_posf_3## Multiple Correspondence Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for categories"
## 2 "$cos2" "Cos2 for categories"
## 3 "$contrib" "contributions of categories"# Coordinates
var_posf_3$coord## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_since_yng_br_0 -0.38947982 0.5041055 -0.1829279 -0.31697315
## ea_read_since_yng_br_1 -0.52466392 0.1824891 0.2256815 -0.05516922
## ea_read_since_yng_br_2 -0.19403149 -0.4985289 0.1723880 0.58306643
## ea_read_since_yng_br_3 0.22486292 -0.5022598 0.3060534 0.81631808
## ea_read_since_yng_br_4 0.41683575 -0.8059556 -0.7284819 -0.55038663
## ea_read_since_yng_br_5 1.73930013 0.1796019 1.6813529 -0.87761728
## ea_read_since_yng_br_6 2.29505313 2.1500334 -1.1859586 1.61054906
## ea_indr_since_yng_br_0 -0.49393341 0.5179402 -0.2759620 -0.43131671
## ea_indr_since_yng_br_1 -0.52417652 0.3060655 0.2959020 0.01164216
## ea_indr_since_yng_br_2 -0.17394904 -0.6087357 0.2314559 0.67811340
## ea_indr_since_yng_br_3 0.31038439 -0.5617723 0.1942725 0.54100984
## ea_indr_since_yng_br_4 0.37247161 -0.7016744 -0.5671506 -0.52095279
## ea_indr_since_yng_br_5 1.60608841 0.3490682 1.3263018 -0.79574706
## ea_indr_since_yng_br_6 1.38345656 1.3905325 -1.0730450 1.45928601
## ea_letter_since_yng_br_0 -0.42789894 0.5072968 -0.2480182 -0.41588788
## ea_letter_since_yng_br_1 -0.48127883 0.1606773 0.3777639 0.19031294
## ea_letter_since_yng_br_2 0.02793906 -0.6174997 0.3184133 0.63631891
## ea_letter_since_yng_br_3 0.42307855 -0.7716526 0.1939514 0.92996815
## ea_letter_since_yng_br_4 0.76860991 -1.0266130 -1.0143352 -0.87032389
## ea_letter_since_yng_br_5 3.09042712 0.7263406 2.9169621 -1.63079891
## ea_letter_since_yng_br_6 2.84738039 2.5676528 -1.3506203 1.92217494
## ea_tv_since_yng_br_0 -0.83760867 0.8968317 -0.2721652 -0.76887895
## ea_tv_since_yng_br_1 -0.73515564 0.3828409 0.4571679 -0.03072843
## ea_tv_since_yng_br_2 -0.38700969 -0.4131912 0.4179814 0.70732483
## ea_tv_since_yng_br_3 0.07266170 -0.5796236 0.3174421 1.12612066
## ea_tv_since_yng_br_4 0.38345415 -0.9169333 -0.7784602 -0.55921879
## ea_tv_since_yng_br_5 1.83819479 0.2184887 1.6256977 -1.08322677
## ea_tv_since_yng_br_6 1.35229031 1.6308003 -1.2070268 0.90350697
## ea_boardg_since_yng_br_0 -0.45144941 0.6104810 -0.3198838 -0.59774538
## ea_boardg_since_yng_br_1 -0.48107973 0.2904157 0.1905918 0.04811567
## ea_boardg_since_yng_br_2 -0.13972254 -0.4961347 0.3288134 0.54607457
## ea_boardg_since_yng_br_3 0.45809211 -0.8142901 -0.1294077 0.46519425
## ea_boardg_since_yng_br_4 0.73178634 -1.1136746 -1.0241459 -0.90195047
## ea_boardg_since_yng_br_5 2.92813416 0.7438877 2.6360872 -1.36614101
## ea_boardg_since_yng_br_6 2.38293473 2.5213455 -1.8645690 2.20186597
## ea_vidg_since_yng_br_0 -0.89780568 0.9394967 -0.2459625 -0.88306753
## ea_vidg_since_yng_br_1 -0.76096614 0.4676019 0.4023607 -0.04254611
## ea_vidg_since_yng_br_2 -0.35657469 -0.3724440 0.5134509 0.85408861
## ea_vidg_since_yng_br_3 0.05828689 -0.5929698 0.2744427 0.96893264
## ea_vidg_since_yng_br_4 0.20380860 -0.9343988 -0.7864771 -0.50044612
## ea_vidg_since_yng_br_5 1.50583892 0.2021363 1.2526532 -0.89318316
## ea_vidg_since_yng_br_6 1.10037838 1.3869646 -1.0850574 0.59795489
## ea_edapp_since_yng_br_0 -0.58074031 0.5338623 -0.1833380 -0.54980688
## ea_edapp_since_yng_br_1 -0.75278568 0.4440752 0.4377963 0.09484225
## ea_edapp_since_yng_br_2 -0.39439896 -0.3250220 0.5249969 0.86833445
## ea_edapp_since_yng_br_3 0.23074024 -0.6301666 0.3011992 1.06246607
## ea_edapp_since_yng_br_4 0.24098780 -1.0500031 -0.7009088 -0.38756012
## ea_edapp_since_yng_br_5 1.57189270 0.1625114 1.3503295 -0.81831845
## ea_edapp_since_yng_br_6 1.19015879 1.2503477 -1.1195810 0.61094934
## Dim 5
## ea_read_since_yng_br_0 0.468990546
## ea_read_since_yng_br_1 -0.530947483
## ea_read_since_yng_br_2 -0.004186454
## ea_read_since_yng_br_3 0.551151378
## ea_read_since_yng_br_4 -0.257401820
## ea_read_since_yng_br_5 0.169616156
## ea_read_since_yng_br_6 -0.191562175
## ea_indr_since_yng_br_0 0.766150360
## ea_indr_since_yng_br_1 -0.605463955
## ea_indr_since_yng_br_2 0.093251144
## ea_indr_since_yng_br_3 0.594598315
## ea_indr_since_yng_br_4 -0.329895174
## ea_indr_since_yng_br_5 0.069821656
## ea_indr_since_yng_br_6 -0.167881665
## ea_letter_since_yng_br_0 0.520596470
## ea_letter_since_yng_br_1 -0.820898873
## ea_letter_since_yng_br_2 0.186589119
## ea_letter_since_yng_br_3 0.901530849
## ea_letter_since_yng_br_4 -0.572207190
## ea_letter_since_yng_br_5 0.235479896
## ea_letter_since_yng_br_6 -0.635834876
## ea_tv_since_yng_br_0 1.056004016
## ea_tv_since_yng_br_1 -1.017491115
## ea_tv_since_yng_br_2 0.031690016
## ea_tv_since_yng_br_3 0.874586946
## ea_tv_since_yng_br_4 -0.251497535
## ea_tv_since_yng_br_5 0.171173528
## ea_tv_since_yng_br_6 -0.308337653
## ea_boardg_since_yng_br_0 0.710527684
## ea_boardg_since_yng_br_1 -0.587300036
## ea_boardg_since_yng_br_2 0.172168840
## ea_boardg_since_yng_br_3 0.490547178
## ea_boardg_since_yng_br_4 -0.492267425
## ea_boardg_since_yng_br_5 0.121034343
## ea_boardg_since_yng_br_6 -0.665354497
## ea_vidg_since_yng_br_0 1.154351643
## ea_vidg_since_yng_br_1 -0.968525267
## ea_vidg_since_yng_br_2 -0.065173327
## ea_vidg_since_yng_br_3 1.071846292
## ea_vidg_since_yng_br_4 -0.240465217
## ea_vidg_since_yng_br_5 0.108776831
## ea_vidg_since_yng_br_6 -0.236708047
## ea_edapp_since_yng_br_0 0.611239207
## ea_edapp_since_yng_br_1 -1.000529691
## ea_edapp_since_yng_br_2 -0.070219176
## ea_edapp_since_yng_br_3 1.034718797
## ea_edapp_since_yng_br_4 -0.240350738
## ea_edapp_since_yng_br_5 -0.052801031
## ea_edapp_since_yng_br_6 -0.317942007var_posf_3$cos2## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_since_yng_br_0 0.0502365007 0.084157395 0.011081780 3.327319e-02
## ea_read_since_yng_br_1 0.0935565745 0.011318424 0.017310272 1.034441e-03
## ea_read_since_yng_br_2 0.0065381451 0.043160932 0.005160884 5.903998e-02
## ea_read_since_yng_br_3 0.0058730547 0.029301186 0.010879850 7.740111e-02
## ea_read_since_yng_br_4 0.0329355902 0.123128268 0.100594242 5.742107e-02
## ea_read_since_yng_br_5 0.1660467902 0.001770531 0.155166925 4.227579e-02
## ea_read_since_yng_br_6 0.1862165766 0.163426798 0.049724666 9.170241e-02
## ea_indr_since_yng_br_0 0.0597578865 0.065707910 0.018653360 4.556706e-02
## ea_indr_since_yng_br_1 0.1007457089 0.034347896 0.032104602 4.969797e-05
## ea_indr_since_yng_br_2 0.0049191133 0.060242135 0.008709223 7.475624e-02
## ea_indr_since_yng_br_3 0.0113854553 0.037296772 0.004460395 3.459083e-02
## ea_indr_since_yng_br_4 0.0308913152 0.109627946 0.071622060 6.042919e-02
## ea_indr_since_yng_br_5 0.1603846630 0.007576079 0.109372635 3.937078e-02
## ea_indr_since_yng_br_6 0.0981513871 0.099157984 0.059047467 1.092059e-01
## ea_letter_since_yng_br_0 0.0922202585 0.129618850 0.030982112 8.711570e-02
## ea_letter_since_yng_br_1 0.0794013128 0.008850001 0.048918718 1.241569e-02
## ea_letter_since_yng_br_2 0.0001479651 0.072278490 0.019218426 7.675120e-02
## ea_letter_since_yng_br_3 0.0175799116 0.058481474 0.003694543 8.493972e-02
## ea_letter_since_yng_br_4 0.0771030235 0.137553914 0.134283425 9.886015e-02
## ea_letter_since_yng_br_5 0.2224797953 0.012289499 0.198205243 6.195187e-02
## ea_letter_since_yng_br_6 0.1888619820 0.153576996 0.042493264 8.606754e-02
## ea_tv_since_yng_br_0 0.1202722768 0.137881224 0.012698380 1.013443e-01
## ea_tv_since_yng_br_1 0.1323783638 0.035900053 0.051192922 2.312805e-04
## ea_tv_since_yng_br_2 0.0280469511 0.031970108 0.032715675 9.368710e-02
## ea_tv_since_yng_br_3 0.0007556480 0.048084005 0.014422399 1.815007e-01
## ea_tv_since_yng_br_4 0.0433372466 0.247804939 0.178610621 9.217177e-02
## ea_tv_since_yng_br_5 0.2540127350 0.003588645 0.198679017 8.820865e-02
## ea_tv_since_yng_br_6 0.1513175817 0.220065059 0.120554483 6.754801e-02
## ea_boardg_since_yng_br_0 0.0525145769 0.096029794 0.026366117 9.206491e-02
## ea_boardg_since_yng_br_1 0.1018976309 0.037133860 0.015993310 1.019302e-03
## ea_boardg_since_yng_br_2 0.0059686284 0.075255946 0.033055271 9.116864e-02
## ea_boardg_since_yng_br_3 0.0248002636 0.078362626 0.001979115 2.557522e-02
## ea_boardg_since_yng_br_4 0.0536469221 0.124248995 0.105075113 8.149700e-02
## ea_boardg_since_yng_br_5 0.2882006602 0.018600635 0.233578337 6.273416e-02
## ea_boardg_since_yng_br_6 0.1516762047 0.169807895 0.092864579 1.295015e-01
## ea_vidg_since_yng_br_0 0.1035300039 0.113368419 0.007770326 1.001589e-01
## ea_vidg_since_yng_br_1 0.1331859777 0.050289856 0.037235650 4.163394e-04
## ea_vidg_since_yng_br_2 0.0243941961 0.026613831 0.050580522 1.399559e-01
## ea_vidg_since_yng_br_3 0.0004575782 0.047357500 0.010144412 1.264476e-01
## ea_vidg_since_yng_br_4 0.0123562241 0.259719954 0.183997919 7.449985e-02
## ea_vidg_since_yng_br_5 0.2406214036 0.004335766 0.166509458 8.465610e-02
## ea_vidg_since_yng_br_6 0.1260831044 0.200310436 0.122596541 3.723142e-02
## ea_edapp_since_yng_br_0 0.1059340121 0.089522054 0.010557869 9.494931e-02
## ea_edapp_since_yng_br_1 0.1087246930 0.037835419 0.036773053 1.725795e-03
## ea_edapp_since_yng_br_2 0.0219329144 0.014895338 0.038863172 1.063161e-01
## ea_edapp_since_yng_br_3 0.0059691605 0.044522275 0.010171245 1.265601e-01
## ea_edapp_since_yng_br_4 0.0149641412 0.284081427 0.126585734 3.870257e-02
## ea_edapp_since_yng_br_5 0.2330157895 0.002490617 0.171956680 6.315158e-02
## ea_edapp_since_yng_br_6 0.1308292231 0.144396469 0.115772660 3.447509e-02
## Dim 5
## ea_read_since_yng_br_0 7.284129e-02
## ea_read_since_yng_br_1 9.581093e-02
## ea_read_since_yng_br_2 3.043706e-06
## ea_read_since_yng_br_3 3.528338e-02
## ea_read_since_yng_br_4 1.255911e-02
## ea_read_since_yng_br_5 1.579123e-03
## ea_read_since_yng_br_6 1.297336e-03
## ea_indr_since_yng_br_0 1.437760e-01
## ea_indr_since_yng_br_1 1.344151e-01
## ea_indr_since_yng_br_2 1.413680e-03
## ea_indr_since_yng_br_3 4.178285e-02
## ea_indr_since_yng_br_4 2.423271e-02
## ea_indr_since_yng_br_5 3.031128e-04
## ea_indr_since_yng_br_6 1.445346e-03
## ea_letter_since_yng_br_0 1.365043e-01
## ea_letter_since_yng_br_1 2.310008e-01
## ea_letter_since_yng_br_2 6.599456e-03
## ea_letter_since_yng_br_3 7.982443e-02
## ea_letter_since_yng_br_4 4.273326e-02
## ea_letter_since_yng_br_5 1.291699e-03
## ea_letter_since_yng_br_6 9.417644e-03
## ea_tv_since_yng_br_0 1.911676e-01
## ea_tv_since_yng_br_1 2.535827e-01
## ea_tv_since_yng_br_2 1.880559e-04
## ea_tv_since_yng_br_3 1.094749e-01
## ea_tv_since_yng_br_4 1.864240e-02
## ea_tv_since_yng_br_5 2.202651e-03
## ea_tv_since_yng_br_6 7.866882e-03
## ea_boardg_since_yng_br_0 1.300839e-01
## ea_boardg_since_yng_br_1 1.518623e-01
## ea_boardg_since_yng_br_2 9.062556e-03
## ea_boardg_since_yng_br_3 2.843886e-02
## ea_boardg_since_yng_br_4 2.427607e-02
## ea_boardg_since_yng_br_5 4.924139e-04
## ea_boardg_since_yng_br_6 1.182495e-02
## ea_vidg_since_yng_br_0 1.711503e-01
## ea_vidg_since_yng_br_1 2.157495e-01
## ea_vidg_since_yng_br_2 8.149393e-04
## ea_vidg_since_yng_br_3 1.547350e-01
## ea_vidg_since_yng_br_4 1.720067e-02
## ea_vidg_since_yng_br_5 1.255596e-03
## ea_vidg_since_yng_br_6 5.834435e-03
## ea_edapp_since_yng_br_0 1.173529e-01
## ea_edapp_since_yng_br_1 1.920638e-01
## ea_edapp_since_yng_br_2 6.952425e-04
## ea_edapp_since_yng_br_3 1.200359e-01
## ea_edapp_since_yng_br_4 1.488513e-02
## ea_edapp_since_yng_br_5 2.629204e-04
## ea_edapp_since_yng_br_6 9.336643e-03var_posf_3$contrib## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_since_yng_br_0 0.97859862 1.81334400 0.2823522 0.850303642
## ea_read_since_yng_br_1 1.81063220 0.24229507 0.4381837 0.026263699
## ea_read_since_yng_br_2 0.14445384 1.05479495 0.1491403 1.711256515
## ea_read_since_yng_br_3 0.13644540 0.75297878 0.3306086 2.359046235
## ea_read_since_yng_br_4 0.71795832 2.96889099 2.8681593 1.642099500
## ea_read_since_yng_br_5 4.08171128 0.04814132 4.9889289 1.363320471
## ea_read_since_yng_br_6 4.66388707 4.52747078 1.6289132 3.013039765
## ea_indr_since_yng_br_0 1.24470209 1.51387683 0.5081871 1.245132580
## ea_indr_since_yng_br_1 1.91153526 0.72087273 0.7967451 0.001237054
## ea_indr_since_yng_br_2 0.10971999 1.48628455 0.2540824 2.187463212
## ea_indr_since_yng_br_3 0.26403186 0.95670903 0.1352931 1.052352722
## ea_indr_since_yng_br_4 0.65516038 2.57178821 1.9868011 1.681327839
## ea_indr_since_yng_br_5 3.91547668 0.20458274 3.4924231 1.260927837
## ea_indr_since_yng_br_6 2.42100424 2.70538759 1.9050077 3.533781873
## ea_letter_since_yng_br_0 1.59035063 2.47250632 0.6988339 1.970867328
## ea_letter_since_yng_br_1 1.53333272 0.18904045 1.2356064 0.314538606
## ea_letter_since_yng_br_2 0.00322547 1.74279197 0.5479589 2.194893182
## ea_letter_since_yng_br_3 0.41509543 1.52739820 0.1141007 2.631094750
## ea_letter_since_yng_br_4 1.76853456 3.48993864 4.0286628 2.974800206
## ea_letter_since_yng_br_5 5.63778215 0.34447252 6.5694531 2.059522364
## ea_letter_since_yng_br_6 4.78588498 4.30473656 1.4084265 2.861221675
## ea_tv_since_yng_br_0 2.66236796 3.37606168 0.3676607 2.943039530
## ea_tv_since_yng_br_1 2.75732019 0.82711897 1.3946861 0.006319804
## ea_tv_since_yng_br_2 0.61257414 0.77236004 0.9345989 2.684399729
## ea_tv_since_yng_br_3 0.01714135 1.20650422 0.4279169 5.401299099
## ea_tv_since_yng_br_4 0.86795691 5.48971131 4.6788648 2.421749397
## ea_tv_since_yng_br_5 6.12625065 0.09573538 6.2674033 2.790903846
## ea_tv_since_yng_br_6 3.62393976 5.82968873 3.7763419 2.122259683
## ea_boardg_since_yng_br_0 1.08275942 2.19008412 0.7110410 2.490236831
## ea_boardg_since_yng_br_1 1.83457387 0.73950977 0.3766221 0.024075123
## ea_boardg_since_yng_br_2 0.11853284 1.65313250 0.8586205 2.375221089
## ea_boardg_since_yng_br_3 0.57512498 2.01009976 0.0600307 0.778071810
## ea_boardg_since_yng_br_4 1.26444476 3.23929599 3.2393008 2.519944969
## ea_boardg_since_yng_br_5 7.23028105 0.51616752 7.6645937 2.064708297
## ea_boardg_since_yng_br_6 3.83078270 4.74384664 3.0677257 4.290811059
## ea_vidg_since_yng_br_0 2.37906318 2.88160556 0.2335476 3.019419972
## ea_vidg_since_yng_br_1 2.80783608 1.17272542 1.0267591 0.011514767
## ea_vidg_since_yng_br_2 0.53073711 0.64047625 1.4393706 3.994649721
## ea_vidg_since_yng_br_3 0.01045702 1.19710983 0.3032255 3.790946695
## ea_vidg_since_yng_br_4 0.24694905 5.74155590 4.8098426 1.953311062
## ea_vidg_since_yng_br_5 5.64095051 0.11243112 5.1056767 2.603578730
## ea_vidg_since_yng_br_6 2.96111539 5.20360966 3.7659368 1.147103204
## ea_edapp_since_yng_br_0 2.09037651 1.95398772 0.2724970 2.457962887
## ea_edapp_since_yng_br_1 2.36549010 0.91052984 1.0464513 0.049257977
## ea_edapp_since_yng_br_2 0.49845775 0.37444245 1.1552251 3.169752165
## ea_edapp_since_yng_br_3 0.13918154 1.14828163 0.3101979 3.871323609
## ea_edapp_since_yng_br_4 0.30853462 6.47884572 3.4137619 1.046854560
## ea_edapp_since_yng_br_5 5.52159957 0.06528137 5.3296044 1.963171338
## ea_edapp_since_yng_br_6 3.10567781 3.79149867 3.5946285 1.073621989
## Dim 5
## ea_read_since_yng_br_0 2.2247347981
## ea_read_since_yng_br_1 2.9072767250
## ea_read_since_yng_br_2 0.0001054369
## ea_read_since_yng_br_3 1.2852286832
## ea_read_since_yng_br_4 0.4292476682
## ea_read_since_yng_br_5 0.0608615558
## ea_read_since_yng_br_6 0.0509445176
## ea_indr_since_yng_br_0 4.6953938981
## ea_indr_since_yng_br_1 3.9987032132
## ea_indr_since_yng_br_2 0.0494385161
## ea_indr_since_yng_br_3 1.5192153705
## ea_indr_since_yng_br_4 0.8058024607
## ea_indr_since_yng_br_5 0.0116022289
## ea_indr_since_yng_br_6 0.0558967335
## ea_letter_since_yng_br_0 3.6908661828
## ea_letter_since_yng_br_1 6.9941929912
## ea_letter_since_yng_br_2 0.2255575382
## ea_letter_since_yng_br_3 2.9551703052
## ea_letter_since_yng_br_4 1.5368221490
## ea_letter_since_yng_br_5 0.0513209097
## ea_letter_since_yng_br_6 0.3741755167
## ea_tv_since_yng_br_0 6.6348680373
## ea_tv_since_yng_br_1 8.2814286075
## ea_tv_since_yng_br_2 0.0064398434
## ea_tv_since_yng_br_3 3.8936359910
## ea_tv_since_yng_br_4 0.5854019200
## ea_tv_since_yng_br_5 0.0832914267
## ea_tv_since_yng_br_6 0.2953994107
## ea_boardg_since_yng_br_0 4.2052439333
## ea_boardg_since_yng_br_1 4.2868328039
## ea_boardg_since_yng_br_2 0.2821826066
## ea_boardg_since_yng_br_3 1.0340310053
## ea_boardg_since_yng_br_4 0.8971165393
## ea_boardg_since_yng_br_5 0.0193689407
## ea_boardg_since_yng_br_6 0.4682575802
## ea_vidg_since_yng_br_0 6.1664175872
## ea_vidg_since_yng_br_1 7.1314590185
## ea_vidg_since_yng_br_2 0.0277992846
## ea_vidg_since_yng_br_3 5.5442974297
## ea_vidg_since_yng_br_4 0.5389919566
## ea_vidg_since_yng_br_5 0.0461512490
## ea_vidg_since_yng_br_6 0.2148387802
## ea_edapp_since_yng_br_0 3.6307670280
## ea_edapp_since_yng_br_1 6.5516968948
## ea_edapp_since_yng_br_2 0.0247732903
## ea_edapp_since_yng_br_3 4.3882868371
## ea_edapp_since_yng_br_4 0.4811938933
## ea_edapp_since_yng_br_5 0.0097683073
## ea_edapp_since_yng_br_6 0.3475023989#Correlação entre variáveis e dimensões principais
correlação entre as variáveis e as dimensões principais do MCA Pode-se observar que para as duas principais dimensões, a maioria das variáveis têm um correlação alta, destoando um pouco as variáveis ea_read_since_yng_br e ea_indr_since_yng_br.
fviz_mca_var(res.mca_posf_3, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
head(round(var_posf_3$coord, 2), 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_since_yng_br_0 -0.39 0.50 -0.18 -0.32 0.47
## ea_read_since_yng_br_1 -0.52 0.18 0.23 -0.06 -0.53
## ea_read_since_yng_br_2 -0.19 -0.50 0.17 0.58 0.00
## ea_read_since_yng_br_3 0.22 -0.50 0.31 0.82 0.55Agora visualizando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionárioas pelas principais dimensões.
fviz_mca_var(res.mca_posf_3,
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
head(var_posf_3$cos2, 10)## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_since_yng_br_0 0.050236501 0.084157395 0.011081780 3.327319e-02
## ea_read_since_yng_br_1 0.093556574 0.011318424 0.017310272 1.034441e-03
## ea_read_since_yng_br_2 0.006538145 0.043160932 0.005160884 5.903998e-02
## ea_read_since_yng_br_3 0.005873055 0.029301186 0.010879850 7.740111e-02
## ea_read_since_yng_br_4 0.032935590 0.123128268 0.100594242 5.742107e-02
## ea_read_since_yng_br_5 0.166046790 0.001770531 0.155166925 4.227579e-02
## ea_read_since_yng_br_6 0.186216577 0.163426798 0.049724666 9.170241e-02
## ea_indr_since_yng_br_0 0.059757887 0.065707910 0.018653360 4.556706e-02
## ea_indr_since_yng_br_1 0.100745709 0.034347896 0.032104602 4.969797e-05
## ea_indr_since_yng_br_2 0.004919113 0.060242135 0.008709223 7.475624e-02
## Dim 5
## ea_read_since_yng_br_0 7.284129e-02
## ea_read_since_yng_br_1 9.581093e-02
## ea_read_since_yng_br_2 3.043706e-06
## ea_read_since_yng_br_3 3.528338e-02
## ea_read_since_yng_br_4 1.255911e-02
## ea_read_since_yng_br_5 1.579123e-03
## ea_read_since_yng_br_6 1.297336e-03
## ea_indr_since_yng_br_0 1.437760e-01
## ea_indr_since_yng_br_1 1.344151e-01
## ea_indr_since_yng_br_2 1.413680e-03Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados.
fviz_mca_var(res.mca_posf_3, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
15 principais categorias de variáveis que contribuem para cada uma das principais dimensões:
# Contributions of rows to dimension 1
fviz_contrib(res.mca_pref_3, choice = "var", axes = 1, top = 15)
fviz_contrib(res.mca_posf_3, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.mca_pref_3, choice = "var", axes = 2, top = 15)
fviz_contrib(res.mca_posf_3, choice = "var", axes = 2, top = 15)
fviz_cos2(res.mca_posf_3, choice = "var", axes = 1:2)
head(round(var_posf_3$contrib,2), 49)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_since_yng_br_0 0.98 1.81 0.28 0.85 2.22
## ea_read_since_yng_br_1 1.81 0.24 0.44 0.03 2.91
## ea_read_since_yng_br_2 0.14 1.05 0.15 1.71 0.00
## ea_read_since_yng_br_3 0.14 0.75 0.33 2.36 1.29
## ea_read_since_yng_br_4 0.72 2.97 2.87 1.64 0.43
## ea_read_since_yng_br_5 4.08 0.05 4.99 1.36 0.06
## ea_read_since_yng_br_6 4.66 4.53 1.63 3.01 0.05
## ea_indr_since_yng_br_0 1.24 1.51 0.51 1.25 4.70
## ea_indr_since_yng_br_1 1.91 0.72 0.80 0.00 4.00
## ea_indr_since_yng_br_2 0.11 1.49 0.25 2.19 0.05
## ea_indr_since_yng_br_3 0.26 0.96 0.14 1.05 1.52
## ea_indr_since_yng_br_4 0.66 2.57 1.99 1.68 0.81
## ea_indr_since_yng_br_5 3.92 0.20 3.49 1.26 0.01
## ea_indr_since_yng_br_6 2.42 2.71 1.91 3.53 0.06
## ea_letter_since_yng_br_0 1.59 2.47 0.70 1.97 3.69
## ea_letter_since_yng_br_1 1.53 0.19 1.24 0.31 6.99
## ea_letter_since_yng_br_2 0.00 1.74 0.55 2.19 0.23
## ea_letter_since_yng_br_3 0.42 1.53 0.11 2.63 2.96
## ea_letter_since_yng_br_4 1.77 3.49 4.03 2.97 1.54
## ea_letter_since_yng_br_5 5.64 0.34 6.57 2.06 0.05
## ea_letter_since_yng_br_6 4.79 4.30 1.41 2.86 0.37
## ea_tv_since_yng_br_0 2.66 3.38 0.37 2.94 6.63
## ea_tv_since_yng_br_1 2.76 0.83 1.39 0.01 8.28
## ea_tv_since_yng_br_2 0.61 0.77 0.93 2.68 0.01
## ea_tv_since_yng_br_3 0.02 1.21 0.43 5.40 3.89
## ea_tv_since_yng_br_4 0.87 5.49 4.68 2.42 0.59
## ea_tv_since_yng_br_5 6.13 0.10 6.27 2.79 0.08
## ea_tv_since_yng_br_6 3.62 5.83 3.78 2.12 0.30
## ea_boardg_since_yng_br_0 1.08 2.19 0.71 2.49 4.21
## ea_boardg_since_yng_br_1 1.83 0.74 0.38 0.02 4.29
## ea_boardg_since_yng_br_2 0.12 1.65 0.86 2.38 0.28
## ea_boardg_since_yng_br_3 0.58 2.01 0.06 0.78 1.03
## ea_boardg_since_yng_br_4 1.26 3.24 3.24 2.52 0.90
## ea_boardg_since_yng_br_5 7.23 0.52 7.66 2.06 0.02
## ea_boardg_since_yng_br_6 3.83 4.74 3.07 4.29 0.47
## ea_vidg_since_yng_br_0 2.38 2.88 0.23 3.02 6.17
## ea_vidg_since_yng_br_1 2.81 1.17 1.03 0.01 7.13
## ea_vidg_since_yng_br_2 0.53 0.64 1.44 3.99 0.03
## ea_vidg_since_yng_br_3 0.01 1.20 0.30 3.79 5.54
## ea_vidg_since_yng_br_4 0.25 5.74 4.81 1.95 0.54
## ea_vidg_since_yng_br_5 5.64 0.11 5.11 2.60 0.05
## ea_vidg_since_yng_br_6 2.96 5.20 3.77 1.15 0.21
## ea_edapp_since_yng_br_0 2.09 1.95 0.27 2.46 3.63
## ea_edapp_since_yng_br_1 2.37 0.91 1.05 0.05 6.55
## ea_edapp_since_yng_br_2 0.50 0.37 1.16 3.17 0.02
## ea_edapp_since_yng_br_3 0.14 1.15 0.31 3.87 4.39
## ea_edapp_since_yng_br_4 0.31 6.48 3.41 1.05 0.48
## ea_edapp_since_yng_br_5 5.52 0.07 5.33 1.96 0.01
## ea_edapp_since_yng_br_6 3.11 3.79 3.59 1.07 0.3515 principais categorias de variáveis que contribuem para as dimensões:
# Contributions of rows to dimension 1
fviz_contrib(res.mca_posf_3, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.mca_posf_3, choice = "var", axes = 2, top = 15)
fviz_contrib(res.mca_posf_3, choice = "var", axes = 1:2, top = 15)
fviz_mca_var(res.mca_posf_3, alpha.var="contrib",
repel = TRUE,
ggtheme = theme_minimal())## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
##Gráfico de indivíduos Resultados
lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:
ind_posf_3 <- get_mca_ind(res.mca_posf_3)
ind_posf_3## Multiple Correspondence Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"colorir os indivíduos por seus valores de cos2:
fviz_mca_ind(res.mca_posf_3, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping (slow if many points)
ggtheme = theme_minimal())## Warning: ggrepel: 531 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Cos2 of individuals
fviz_cos2(res.mca_posf_3, choice = "ind", axes = 1:2, top = 20)
# Contribution of individuals to the dimensions
fviz_contrib(res.mca_posf_3, choice = "ind", axes = 1:2, top = 20)
PANDEMIA COMPLETA
db_bloco_3f <- as.data.frame(lapply(db_bloco_3, as.factor))summary(db_bloco_3f)## ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br ea_tv_b4_yng_br
## 0:113 0:114 0:198 0:121
## 1:181 1:163 1:188 1:137
## 2:106 2:115 2:113 2:126
## 3: 51 3: 64 3: 36 3: 55
## 4:114 4:109 4: 57 4:111
## 5: 39 5: 34 5: 18 5: 42
## 6: 11 6: 16 6: 5 6: 23
## ea_boardg_b4_yng_br ea_vidg_b4_yng_br ea_edapp_b4_yng_br ea_read_since_yng_br
## 0:158 0:115 0:156 0:153
## 1:208 1:150 1:132 1:156
## 2:135 2:127 2:110 2: 91
## 3: 55 3: 60 3: 69 3: 64
## 4: 32 4: 97 4: 87 4: 98
## 5: 16 5: 39 5: 32 5: 32
## 6: 11 6: 27 6: 29 6: 21
## ea_indr_since_yng_br ea_letter_since_yng_br ea_tv_since_yng_br
## 0:121 0:206 0: 90
## 1:165 1:157 1:121
## 2: 86 2: 98 2: 97
## 3: 65 3: 55 3: 77
## 4:112 4: 71 4:140
## 5: 36 5: 14 5: 43
## 6: 30 6: 14 6: 47
## ea_boardg_since_yng_br ea_vidg_since_yng_br ea_edapp_since_yng_br
## 0:126 0: 70 0:147
## 1:188 1:115 1: 99
## 2:144 2: 99 2: 76
## 3: 65 3: 73 3: 62
## 4: 56 4:141 4:126
## 5: 20 5: 59 5: 53
## 6: 16 6: 58 6: 52Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.
for (i in 1:14) {
plot(db_bloco_3f[,i], main=colnames(db_bloco_3f)[i],
ylab = "Count", col="steelblue", las = 2)
}
MCA(db_bloco_3f,ncp = 5, graph = TRUE) 
## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 14 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"res.mca_3f <- MCA(db_bloco_3f, graph = FALSE)
print(res.mca_3f)## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 14 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "intermediate results"
## 12 "$call$marge.col" "weights of columns"
## 13 "$call$marge.li" "weights of rows"#Visualização e interpretação
Pode-se observar que juntando os blocos pré e pós pandemia, conseguiremos explicar mais de 50% da variância apenas com 15 dimensões.
eig.val_3f <- get_eigenvalue(res.mca_3f)
eig.val_3f## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 0.486127819 8.1021303 8.10213
## Dim.2 0.401096817 6.6849469 14.78708
## Dim.3 0.326654859 5.4442477 20.23132
## Dim.4 0.293692829 4.8948805 25.12621
## Dim.5 0.270513692 4.5085615 29.63477
## Dim.6 0.179836730 2.9972788 32.63205
## Dim.7 0.165824887 2.7637481 35.39579
## Dim.8 0.132285531 2.2047589 37.60055
## Dim.9 0.126813795 2.1135633 39.71412
## Dim.10 0.120100961 2.0016827 41.71580
## Dim.11 0.115450286 1.9241714 43.63997
## Dim.12 0.113702571 1.8950429 45.53501
## Dim.13 0.108293113 1.8048852 47.33990
## Dim.14 0.104269529 1.7378255 49.07772
## Dim.15 0.102230520 1.7038420 50.78157
## Dim.16 0.096601558 1.6100260 52.39159
## Dim.17 0.094781970 1.5796995 53.97129
## Dim.18 0.091700816 1.5283469 55.49964
## Dim.19 0.089830289 1.4971715 56.99681
## Dim.20 0.087475022 1.4579170 58.45473
## Dim.21 0.084891864 1.4148644 59.86959
## Dim.22 0.079099394 1.3183232 61.18791
## Dim.23 0.077654710 1.2942452 62.48216
## Dim.24 0.075879914 1.2646652 63.74682
## Dim.25 0.075015944 1.2502657 64.99709
## Dim.26 0.072455869 1.2075978 66.20469
## Dim.27 0.071697915 1.1949652 67.39965
## Dim.28 0.069532270 1.1588712 68.55852
## Dim.29 0.066993323 1.1165554 69.67508
## Dim.30 0.066108456 1.1018076 70.77689
## Dim.31 0.063688460 1.0614743 71.83836
## Dim.32 0.062816679 1.0469447 72.88531
## Dim.33 0.061114223 1.0185704 73.90388
## Dim.34 0.059571821 0.9928637 74.89674
## Dim.35 0.058675494 0.9779249 75.87467
## Dim.36 0.055755478 0.9292580 76.80392
## Dim.37 0.055318768 0.9219795 77.72590
## Dim.38 0.053727342 0.8954557 78.62136
## Dim.39 0.051445529 0.8574255 79.47878
## Dim.40 0.049988277 0.8331379 80.31192
## Dim.41 0.048942947 0.8157158 81.12764
## Dim.42 0.047794227 0.7965704 81.92421
## Dim.43 0.046513928 0.7752321 82.69944
## Dim.44 0.045542450 0.7590408 83.45848
## Dim.45 0.043884725 0.7314121 84.18989
## Dim.46 0.041747200 0.6957867 84.88568
## Dim.47 0.041021425 0.6836904 85.56937
## Dim.48 0.039447633 0.6574606 86.22683
## Dim.49 0.038505160 0.6417527 86.86858
## Dim.50 0.038284159 0.6380693 87.50665
## Dim.51 0.036667704 0.6111284 88.11778
## Dim.52 0.035516679 0.5919447 88.70973
## Dim.53 0.034232561 0.5705427 89.28027
## Dim.54 0.034067177 0.5677863 89.84805
## Dim.55 0.033719370 0.5619895 90.41004
## Dim.56 0.032451072 0.5408512 90.95090
## Dim.57 0.030985855 0.5164309 91.46733
## Dim.58 0.029861745 0.4976957 91.96502
## Dim.59 0.029322050 0.4887008 92.45372
## Dim.60 0.029003904 0.4833984 92.93712
## Dim.61 0.027996325 0.4666054 93.40373
## Dim.62 0.027727139 0.4621190 93.86585
## Dim.63 0.025048957 0.4174826 94.28333
## Dim.64 0.024675591 0.4112598 94.69459
## Dim.65 0.024418996 0.4069833 95.10157
## Dim.66 0.023853676 0.3975613 95.49913
## Dim.67 0.022813588 0.3802265 95.87936
## Dim.68 0.021309303 0.3551551 96.23451
## Dim.69 0.019835157 0.3305860 96.56510
## Dim.70 0.019154128 0.3192355 96.88434
## Dim.71 0.018316042 0.3052674 97.18960
## Dim.72 0.017884388 0.2980731 97.48768
## Dim.73 0.017056871 0.2842812 97.77196
## Dim.74 0.016371185 0.2728531 98.04481
## Dim.75 0.015510924 0.2585154 98.30333
## Dim.76 0.014944094 0.2490682 98.55239
## Dim.77 0.014362895 0.2393816 98.79178
## Dim.78 0.012924055 0.2154009 99.00718
## Dim.79 0.012090393 0.2015065 99.20868
## Dim.80 0.011101960 0.1850327 99.39372
## Dim.81 0.010105151 0.1684192 99.56214
## Dim.82 0.009712734 0.1618789 99.72401
## Dim.83 0.008319556 0.1386593 99.86267
## Dim.84 0.008239601 0.1373267 100.00000Porcentagens de inércia explicadas por cada dimensão MCA
fviz_screeplot(res.mca_3f, addlabels = TRUE, ylim = c(0, 10))
Biplot de indivíduos e categorias de variáveis: Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.
fviz_mca_biplot(res.mca_3f,
repel = TRUE, # Avoid text overlapping (slow if many point)
ggtheme = theme_minimal())## Warning: ggrepel: 525 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
#Gráfico de variáveis #Resultados Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:
var_3f <- get_mca_var(res.mca_3f)
var_3f## Multiple Correspondence Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for categories"
## 2 "$cos2" "Cos2 for categories"
## 3 "$contrib" "contributions of categories"# Coordinates
var_3f$coord## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 -0.34023445 0.6098388 -0.566058362 0.0013399019
## ea_read_b4_ALL_br_1 -0.32793404 0.2336533 0.284721667 -0.0213279464
## ea_read_b4_ALL_br_2 -0.21226636 -0.4560361 0.400784805 0.0822269791
## ea_read_b4_ALL_br_3 -0.07987968 -0.3566814 0.452358046 -0.0398479487
## ea_read_b4_ALL_br_4 0.27569655 -0.6721408 -0.565125401 0.0474921975
## ea_read_b4_ALL_br_5 1.92469051 0.4468995 0.153089199 -0.7482083129
## ea_read_b4_ALL_br_6 1.62584673 1.3202300 0.484576411 1.8901044242
## ea_indr_b4_ALL_br_0 -0.34400898 0.5556029 -0.810089589 0.0948216544
## ea_indr_b4_ALL_br_1 -0.47711642 0.2697326 0.484359728 -0.1747165407
## ea_indr_b4_ALL_br_2 -0.23413997 -0.2632049 0.378000616 0.1028707860
## ea_indr_b4_ALL_br_3 0.13291402 -0.6127583 0.304454689 0.1048244210
## ea_indr_b4_ALL_br_4 0.47445054 -0.6559181 -0.621653592 -0.0521056446
## ea_indr_b4_ALL_br_5 1.79719449 0.5153544 0.372308403 -0.2883380504
## ea_indr_b4_ALL_br_6 1.41167991 1.0095608 0.346635145 0.9133270736
## ea_letter_b4_yng_br_0 -0.37890639 0.4302642 -0.578087172 -0.0364202393
## ea_letter_b4_yng_br_1 -0.35527993 0.2384165 0.420939905 -0.0895948018
## ea_letter_b4_yng_br_2 0.01940297 -0.6306630 0.558557172 0.2232439968
## ea_letter_b4_yng_br_3 0.29218387 -1.0130107 0.135984375 0.0518375591
## ea_letter_b4_yng_br_4 0.96463080 -0.9813624 -0.868411542 0.1535683511
## ea_letter_b4_yng_br_5 3.44546114 1.4928609 0.619714955 -1.4286374584
## ea_letter_b4_yng_br_6 2.42053646 1.3569676 1.131349700 2.7848769183
## ea_tv_b4_yng_br_0 -0.57980860 0.6653764 -0.788107939 -0.1519870979
## ea_tv_b4_yng_br_1 -0.53847900 0.3734180 0.487395760 -0.2410335950
## ea_tv_b4_yng_br_2 -0.27733778 -0.4155502 0.582630238 0.1378019331
## ea_tv_b4_yng_br_3 0.07253817 -0.6772478 0.386914446 0.4496439949
## ea_tv_b4_yng_br_4 0.49237484 -0.8851662 -0.699380446 0.1430021734
## ea_tv_b4_yng_br_5 2.08203899 0.6474819 0.279533229 -1.5007580221
## ea_tv_b4_yng_br_6 1.42539909 1.2607980 -0.009262138 2.4555292931
## ea_boardg_b4_yng_br_0 -0.28503760 0.6479613 -0.604049646 0.0917366825
## ea_boardg_b4_yng_br_1 -0.34850159 0.1231110 0.262485108 -0.1977170478
## ea_boardg_b4_yng_br_2 -0.09044949 -0.5019738 0.466184530 0.0744795643
## ea_boardg_b4_yng_br_3 0.49759438 -0.9292323 -0.081075343 0.1324555020
## ea_boardg_b4_yng_br_4 0.69996504 -1.3801865 -0.992345754 0.3134511941
## ea_boardg_b4_yng_br_5 3.29639769 1.2571104 0.399312147 -1.8202901623
## ea_boardg_b4_yng_br_6 2.47509234 1.3583156 0.703022151 2.5804691537
## ea_vidg_b4_yng_br_0 -0.50941072 0.6058703 -0.776578804 -0.1725638859
## ea_vidg_b4_yng_br_1 -0.54437179 0.3720686 0.509958556 -0.1601312567
## ea_vidg_b4_yng_br_2 -0.26086417 -0.4588276 0.601567642 0.0571843346
## ea_vidg_b4_yng_br_3 0.29250714 -0.6158647 0.186510107 0.3573632430
## ea_vidg_b4_yng_br_4 0.37124869 -0.8831973 -0.891822547 0.0688606439
## ea_vidg_b4_yng_br_5 2.06142544 0.4374692 0.298107005 -1.6071821719
## ea_vidg_b4_yng_br_6 1.45965208 1.4202390 0.003840595 2.6355908351
## ea_edapp_b4_yng_br_0 -0.34745330 0.4624115 -0.590953398 -0.1111783123
## ea_edapp_b4_yng_br_1 -0.57276032 0.3362901 0.429722769 -0.1978575987
## ea_edapp_b4_yng_br_2 -0.23470652 -0.3656896 0.729419549 0.0175853228
## ea_edapp_b4_yng_br_3 0.11839714 -0.5080980 0.270842551 0.4242347111
## ea_edapp_b4_yng_br_4 0.41576561 -1.1017346 -0.837464159 0.0066314183
## ea_edapp_b4_yng_br_5 1.98852647 0.4758632 0.082218231 -1.5662779830
## ea_edapp_b4_yng_br_6 1.64313572 1.3579800 0.233428850 2.1309793529
## ea_read_since_yng_br_0 -0.35678948 0.4595136 -0.486136535 0.0355513424
## ea_read_since_yng_br_1 -0.45119985 0.2142820 0.212622730 -0.2114990452
## ea_read_since_yng_br_2 -0.26082213 -0.4274673 0.562021780 0.0928508065
## ea_read_since_yng_br_3 0.09865565 -0.4517482 0.547457530 0.2101608347
## ea_read_since_yng_br_4 0.41584825 -0.8192976 -0.685005854 0.1996363999
## ea_read_since_yng_br_5 1.70063976 0.2991336 0.200956881 -1.4966860473
## ea_read_since_yng_br_6 2.24872463 1.6569870 0.748973026 1.6183031035
## ea_indr_since_yng_br_0 -0.44249567 0.5752882 -0.720319014 0.1088063755
## ea_indr_since_yng_br_1 -0.42280819 0.3006164 0.317309122 -0.2062697394
## ea_indr_since_yng_br_2 -0.28990911 -0.5476669 0.662607080 0.1105708962
## ea_indr_since_yng_br_3 0.11552045 -0.5281234 0.401088749 0.1233349176
## ea_indr_since_yng_br_4 0.41003999 -0.7472877 -0.591352714 0.0001248723
## ea_indr_since_yng_br_5 1.51769088 0.4409849 0.071350297 -1.0379040151
## ea_indr_since_yng_br_6 1.33891107 1.0012189 0.513683709 1.3564542564
## ea_letter_since_yng_br_0 -0.39758235 0.5016895 -0.632853178 0.0579527848
## ea_letter_since_yng_br_1 -0.42793142 0.2018179 0.487394560 -0.2208596373
## ea_letter_since_yng_br_2 -0.01619833 -0.5829279 0.692864944 0.0604054509
## ea_letter_since_yng_br_3 0.17236407 -0.7499698 0.616340678 0.3021300799
## ea_letter_since_yng_br_4 0.84250910 -1.1107096 -0.925773281 0.1115444191
## ea_letter_since_yng_br_5 2.90594270 1.0859128 0.329432038 -2.6129498329
## ea_letter_since_yng_br_6 2.90666176 1.9285302 0.940368664 2.0615317638
## ea_tv_since_yng_br_0 -0.70295293 0.9039510 -1.023104598 -0.0652560921
## ea_tv_since_yng_br_1 -0.63905128 0.4397003 0.394390285 -0.3823352238
## ea_tv_since_yng_br_2 -0.40613416 -0.2978866 0.712130404 -0.0165952908
## ea_tv_since_yng_br_3 -0.13056739 -0.5541037 0.762471260 0.3560082491
## ea_tv_since_yng_br_4 0.31129238 -0.9411542 -0.669791253 0.1827638355
## ea_tv_since_yng_br_5 1.78944679 0.2210306 0.017073821 -1.7710965332
## ea_tv_since_yng_br_6 1.47898992 1.2608285 0.204422429 1.6362326030
## ea_boardg_since_yng_br_0 -0.37483318 0.6322085 -0.846850632 0.0596122332
## ea_boardg_since_yng_br_1 -0.38832915 0.3055777 0.239486191 -0.1209616199
## ea_boardg_since_yng_br_2 -0.21861822 -0.4232069 0.531652605 -0.0415933357
## ea_boardg_since_yng_br_3 0.24918789 -0.8375312 0.226195377 0.3165769526
## ea_boardg_since_yng_br_4 0.73932756 -1.2577366 -0.932503429 0.1020314447
## ea_boardg_since_yng_br_5 2.87677359 1.0027223 0.461168988 -2.4981417259
## ea_boardg_since_yng_br_6 2.28630343 1.7908281 0.838494588 2.8056659499
## ea_vidg_since_yng_br_0 -0.74981327 0.9747859 -1.056527295 -0.1652060972
## ea_vidg_since_yng_br_1 -0.64058285 0.5122671 0.359225431 -0.2975519965
## ea_vidg_since_yng_br_2 -0.35580711 -0.2790884 0.780130831 -0.0275065078
## ea_vidg_since_yng_br_3 -0.13787208 -0.5353303 0.620976893 0.2676388676
## ea_vidg_since_yng_br_4 0.16315462 -0.9396854 -0.671506068 0.1976860276
## ea_vidg_since_yng_br_5 1.36644287 0.2296450 -0.035622291 -1.3406781143
## ea_vidg_since_yng_br_6 1.16928606 1.0087880 0.118375712 1.3826671067
## ea_edapp_since_yng_br_0 -0.49390368 0.5562376 -0.673176358 -0.1091843023
## ea_edapp_since_yng_br_1 -0.59242450 0.4553445 0.466753894 -0.2484666531
## ea_edapp_since_yng_br_2 -0.43724701 -0.1945980 0.800621510 -0.0065314499
## ea_edapp_since_yng_br_3 0.03978740 -0.5781787 0.707174640 0.3267230215
## ea_edapp_since_yng_br_4 0.14803600 -1.0641685 -0.520711222 0.1670006086
## ea_edapp_since_yng_br_5 1.44752499 0.1622542 0.082560471 -1.3530915593
## ea_edapp_since_yng_br_6 1.28166268 0.9476205 0.178656454 1.3761467291
## Dim 5
## ea_read_b4_ALL_br_0 0.336852495
## ea_read_b4_ALL_br_1 -0.488053241
## ea_read_b4_ALL_br_2 0.357079718
## ea_read_b4_ALL_br_3 0.572953239
## ea_read_b4_ALL_br_4 -0.215208994
## ea_read_b4_ALL_br_5 0.248839262
## ea_read_b4_ALL_br_6 -0.178969683
## ea_indr_b4_ALL_br_0 0.514293180
## ea_indr_b4_ALL_br_1 -0.604402426
## ea_indr_b4_ALL_br_2 0.474875984
## ea_indr_b4_ALL_br_3 0.475172029
## ea_indr_b4_ALL_br_4 -0.474246407
## ea_indr_b4_ALL_br_5 0.069966726
## ea_indr_b4_ALL_br_6 0.261275906
## ea_letter_b4_yng_br_0 0.328305197
## ea_letter_b4_yng_br_1 -0.622533322
## ea_letter_b4_yng_br_2 0.633848637
## ea_letter_b4_yng_br_3 0.498435816
## ea_letter_b4_yng_br_4 -0.734889767
## ea_letter_b4_yng_br_5 0.347663698
## ea_letter_b4_yng_br_6 -0.381195976
## ea_tv_b4_yng_br_0 0.552954493
## ea_tv_b4_yng_br_1 -0.737294035
## ea_tv_b4_yng_br_2 0.478278727
## ea_tv_b4_yng_br_3 0.688396187
## ea_tv_b4_yng_br_4 -0.526314583
## ea_tv_b4_yng_br_5 0.049369120
## ea_tv_b4_yng_br_6 -0.333726314
## ea_boardg_b4_yng_br_0 0.354020604
## ea_boardg_b4_yng_br_1 -0.425655089
## ea_boardg_b4_yng_br_2 0.415833948
## ea_boardg_b4_yng_br_3 0.060673071
## ea_boardg_b4_yng_br_4 -0.765638490
## ea_boardg_b4_yng_br_5 0.169199528
## ea_boardg_b4_yng_br_6 -0.461850879
## ea_vidg_b4_yng_br_0 0.618221507
## ea_vidg_b4_yng_br_1 -0.684664768
## ea_vidg_b4_yng_br_2 0.484276230
## ea_vidg_b4_yng_br_3 0.550470106
## ea_vidg_b4_yng_br_4 -0.603356134
## ea_vidg_b4_yng_br_5 0.124102199
## ea_vidg_b4_yng_br_6 -0.342277275
## ea_edapp_b4_yng_br_0 0.377836267
## ea_edapp_b4_yng_br_1 -0.647216642
## ea_edapp_b4_yng_br_2 0.479276959
## ea_edapp_b4_yng_br_3 0.517913608
## ea_edapp_b4_yng_br_4 -0.554460177
## ea_edapp_b4_yng_br_5 0.009742579
## ea_edapp_b4_yng_br_6 -0.484141119
## ea_read_since_yng_br_0 0.225845664
## ea_read_since_yng_br_1 -0.556390759
## ea_read_since_yng_br_2 0.334379294
## ea_read_since_yng_br_3 0.648693640
## ea_read_since_yng_br_4 -0.356459919
## ea_read_since_yng_br_5 0.533455348
## ea_read_since_yng_br_6 -0.087611234
## ea_indr_since_yng_br_0 0.540572365
## ea_indr_since_yng_br_1 -0.601796204
## ea_indr_since_yng_br_2 0.429086735
## ea_indr_since_yng_br_3 0.494024172
## ea_indr_since_yng_br_4 -0.418235682
## ea_indr_since_yng_br_5 0.298371592
## ea_indr_since_yng_br_6 0.032503541
## ea_letter_since_yng_br_0 0.283509091
## ea_letter_since_yng_br_1 -0.685230657
## ea_letter_since_yng_br_2 0.493921670
## ea_letter_since_yng_br_3 0.895173705
## ea_letter_since_yng_br_4 -0.746084013
## ea_letter_since_yng_br_5 0.649844295
## ea_letter_since_yng_br_6 -0.327599442
## ea_tv_since_yng_br_0 0.655692258
## ea_tv_since_yng_br_1 -0.906061237
## ea_tv_since_yng_br_2 0.354082006
## ea_tv_since_yng_br_3 0.869480192
## ea_tv_since_yng_br_4 -0.397875032
## ea_tv_since_yng_br_5 0.368099326
## ea_tv_since_yng_br_6 -0.229799773
## ea_boardg_since_yng_br_0 0.383584754
## ea_boardg_since_yng_br_1 -0.593701091
## ea_boardg_since_yng_br_2 0.439894159
## ea_boardg_since_yng_br_3 0.393876211
## ea_boardg_since_yng_br_4 -0.563343343
## ea_boardg_since_yng_br_5 0.642204852
## ea_boardg_since_yng_br_6 -0.434966023
## ea_vidg_since_yng_br_0 0.750241068
## ea_vidg_since_yng_br_1 -0.842954422
## ea_vidg_since_yng_br_2 0.337307240
## ea_vidg_since_yng_br_3 0.823723037
## ea_vidg_since_yng_br_4 -0.377467329
## ea_vidg_since_yng_br_5 0.282445510
## ea_vidg_since_yng_br_6 -0.216270799
## ea_edapp_since_yng_br_0 0.379946956
## ea_edapp_since_yng_br_1 -0.860455455
## ea_edapp_since_yng_br_2 0.349882333
## ea_edapp_since_yng_br_3 0.831054411
## ea_edapp_since_yng_br_4 -0.359594682
## ea_edapp_since_yng_br_5 0.190167488
## ea_edapp_since_yng_br_6 -0.260644040var_3f$cos2## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 2.605741e-02 0.083715289 7.212688e-02 4.041297e-07
## ea_read_b4_ALL_br_1 4.484994e-02 0.022768400 3.380881e-02 1.897086e-04
## ea_read_b4_ALL_br_2 9.383188e-03 0.043309832 3.345111e-02 1.408046e-03
## ea_read_b4_ALL_br_3 5.769839e-04 0.011504085 1.850358e-02 1.435830e-04
## ea_read_b4_ALL_br_4 1.729537e-02 0.102798700 7.267027e-02 5.132296e-04
## ea_read_b4_ALL_br_5 2.508210e-01 0.013522649 1.586833e-03 3.790419e-02
## ea_read_b4_ALL_br_6 4.814098e-02 0.031743510 4.276419e-03 6.506199e-02
## ea_indr_b4_ALL_br_0 2.692816e-02 0.070241882 1.493252e-01 2.045890e-03
## ea_indr_b4_ALL_br_1 8.209145e-02 0.026237111 8.460289e-02 1.100822e-02
## ea_indr_b4_ALL_br_2 1.260895e-02 0.015933669 3.286343e-02 2.433952e-03
## ea_indr_b4_ALL_br_3 2.051965e-03 0.043612076 1.076648e-02 1.276302e-03
## ea_indr_b4_ALL_br_4 4.849064e-02 0.092677689 8.324782e-02 5.848514e-04
## ea_indr_b4_ALL_br_5 1.890136e-01 0.015542281 8.111636e-03 4.865267e-03
## ea_indr_b4_ALL_br_6 5.323112e-02 0.027224389 3.209507e-03 2.228157e-02
## ea_letter_b4_yng_br_0 6.816995e-02 0.087902178 1.586777e-01 6.298175e-04
## ea_letter_b4_yng_br_1 5.557396e-02 0.025026646 7.801357e-02 3.534236e-03
## ea_letter_b4_yng_br_2 8.474440e-05 0.089530176 7.022795e-02 1.121849e-02
## ea_letter_b4_yng_br_3 5.308067e-03 0.063804598 1.149746e-03 1.670756e-04
## ea_letter_b4_yng_br_4 9.505236e-02 0.098378331 7.703566e-02 2.409040e-03
## ea_letter_b4_yng_br_5 3.579257e-01 0.067194984 1.157930e-02 6.153784e-02
## ea_letter_b4_yng_br_6 4.802456e-02 0.015093124 1.049141e-02 6.357000e-02
## ea_tv_b4_yng_br_0 8.234320e-02 0.108440921 1.521352e-01 5.658116e-03
## ea_tv_b4_yng_br_1 8.310559e-02 0.039965316 6.808574e-02 1.665129e-02
## ea_tv_b4_yng_br_2 1.981891e-02 0.044494750 8.746770e-02 4.892967e-03
## ea_tv_b4_yng_br_3 5.167826e-04 0.045047416 1.470295e-02 1.985694e-02
## ea_tv_b4_yng_br_4 5.339298e-02 0.172560778 1.077257e-01 4.503786e-03
## ea_tv_b4_yng_br_5 3.177404e-01 0.030729107 5.727453e-03 1.650882e-01
## ea_tv_b4_yng_br_6 7.893672e-02 0.061758562 3.332949e-06 2.342590e-01
## ea_boardg_b4_yng_br_0 2.808958e-02 0.145157368 1.261497e-01 2.909558e-03
## ea_boardg_b4_yng_br_1 6.206953e-02 0.007745731 3.521099e-02 1.997824e-02
## ea_boardg_b4_yng_br_2 2.300937e-03 0.070868733 6.112350e-02 1.560152e-03
## ea_boardg_b4_yng_br_3 2.431787e-02 0.084805357 6.455832e-04 1.723117e-03
## ea_boardg_b4_yng_br_4 2.689268e-02 0.104557927 5.405146e-02 5.392887e-03
## ea_boardg_b4_yng_br_5 2.902501e-01 0.042212398 4.259104e-03 8.850634e-02
## ea_boardg_b4_yng_br_6 1.115677e-01 0.033601378 9.001062e-03 1.212699e-01
## ea_vidg_b4_yng_br_0 5.968483e-02 0.084428124 1.387072e-01 6.849008e-03
## ea_vidg_b4_yng_br_1 9.559376e-02 0.044656469 8.388959e-02 8.271619e-03
## ea_vidg_b4_yng_br_2 1.770976e-02 0.054787682 9.417873e-02 8.510166e-04
## ea_vidg_b4_yng_br_3 9.249776e-03 0.041004257 3.760651e-03 1.380632e-02
## ea_vidg_b4_yng_br_4 2.580904e-02 0.146068789 1.489357e-01 8.879410e-04
## ea_vidg_b4_yng_br_5 2.877249e-01 0.012957971 6.017090e-03 1.748930e-01
## ea_vidg_b4_yng_br_6 9.783295e-02 0.092620960 6.773038e-07 3.189645e-01
## ea_edapp_b4_yng_br_0 4.103031e-02 0.072672347 1.186912e-01 4.200994e-03
## ea_edapp_b4_yng_br_1 8.965461e-02 0.030906867 5.046654e-02 1.069873e-02
## ea_edapp_b4_yng_br_2 1.199918e-02 0.029129070 1.158927e-01 6.735999e-05
## ea_edapp_b4_yng_br_3 1.771491e-03 0.032625073 9.270224e-03 2.274410e-02
## ea_edapp_b4_yng_br_4 2.848279e-02 0.200004280 1.155627e-01 7.245998e-06
## ea_edapp_b4_yng_br_5 2.170422e-01 0.012429273 3.710374e-04 1.346540e-01
## ea_edapp_b4_yng_br_6 1.336126e-01 0.091261400 2.696556e-03 2.247289e-01
## ea_read_since_yng_br_0 4.215737e-02 0.069927217 7.826471e-02 4.185636e-04
## ea_read_since_yng_br_1 6.919103e-02 0.015605695 1.536496e-02 1.520298e-02
## ea_read_since_yng_br_2 1.181405e-02 0.031733343 5.485502e-02 1.497206e-03
## ea_read_since_yng_br_3 1.130504e-03 0.023703982 3.481202e-02 5.130172e-03
## ea_read_since_yng_br_4 3.277972e-02 0.127238609 8.894552e-02 7.554661e-03
## ea_read_since_yng_br_5 1.587472e-01 0.004911475 2.216599e-03 1.229541e-01
## ea_read_since_yng_br_6 1.787744e-01 0.097066880 1.983194e-02 9.258755e-02
## ea_indr_since_yng_br_0 4.795970e-02 0.081064251 1.270891e-01 2.899794e-03
## ea_indr_since_yng_br_1 6.554782e-02 0.033135741 3.691786e-02 1.560064e-02
## ea_indr_since_yng_br_2 1.366364e-02 0.048761350 7.137645e-02 1.987579e-03
## ea_indr_since_yng_br_3 1.577133e-03 0.032962605 1.901217e-02 1.797723e-03
## ea_indr_since_yng_br_4 3.743712e-02 0.124344258 7.786517e-02 3.472020e-09
## ea_indr_since_yng_br_5 1.432157e-01 0.012091254 3.165305e-04 6.697895e-02
## ea_indr_since_yng_br_6 9.193245e-02 0.051407146 1.353184e-02 9.435734e-02
## ea_letter_since_yng_br_0 7.961559e-02 0.126769259 2.017204e-01 1.691580e-03
## ea_letter_since_yng_br_1 6.277439e-02 0.013962193 8.143208e-02 1.672118e-02
## ea_letter_since_yng_br_2 4.973656e-05 0.064411765 9.099818e-02 6.916522e-04
## ea_letter_since_yng_br_3 2.917885e-03 0.055241083 3.730923e-02 8.965254e-03
## ea_letter_since_yng_br_4 9.264215e-02 0.161012849 1.118584e-01 1.623885e-03
## ea_letter_since_yng_br_5 1.967106e-01 0.027469041 2.528047e-03 1.590434e-01
## ea_letter_since_yng_br_6 1.968079e-01 0.086637609 2.059918e-02 9.899964e-02
## ea_tv_since_yng_br_0 8.471020e-02 0.140078978 1.794417e-01 7.300042e-04
## ea_tv_since_yng_br_1 1.000299e-01 0.047355659 3.809876e-02 3.580528e-02
## ea_tv_since_yng_br_2 3.088738e-02 0.016616667 9.496444e-02 5.157173e-05
## ea_tv_since_yng_br_3 2.439933e-03 0.043943079 8.320615e-02 1.813964e-02
## ea_tv_since_yng_br_4 2.856087e-02 0.261069433 1.322249e-01 9.844983e-03
## ea_tv_since_yng_br_5 2.407188e-01 0.003672631 2.191462e-05 2.358071e-01
## ea_tv_since_yng_br_6 1.810006e-01 0.131541117 3.457854e-03 2.215336e-01
## ea_boardg_since_yng_br_0 3.620243e-02 0.102986985 1.847887e-01 9.156563e-04
## ea_boardg_since_yng_br_1 6.639417e-02 0.041112446 2.525172e-02 6.442066e-03
## ea_boardg_since_yng_br_2 1.461216e-02 0.054757939 8.641666e-02 5.289189e-04
## ea_boardg_since_yng_br_3 7.338454e-03 0.082899639 6.046696e-03 1.184430e-02
## ea_boardg_since_yng_br_4 5.475831e-02 0.158473135 8.711182e-02 1.042904e-03
## ea_boardg_since_yng_br_5 2.781790e-01 0.033796704 7.148801e-03 2.097718e-01
## ea_boardg_since_yng_br_6 1.396243e-01 0.085664511 1.877992e-02 2.102641e-01
## ea_vidg_since_yng_br_0 7.221174e-02 0.122045017 1.433716e-01 3.505530e-03
## ea_vidg_since_yng_br_1 9.437967e-02 0.060356052 2.967987e-02 2.036355e-02
## ea_vidg_since_yng_br_2 2.428929e-02 0.014944071 1.167671e-01 1.451632e-04
## ea_vidg_since_yng_br_3 2.560214e-03 0.038598214 5.193671e-02 9.647659e-03
## ea_vidg_since_yng_br_4 7.918438e-03 0.262667118 1.341345e-01 1.162499e-02
## ea_vidg_since_yng_br_5 1.981345e-01 0.005596174 1.346545e-04 1.907332e-01
## ea_vidg_since_yng_br_6 1.423686e-01 0.105967487 1.459144e-03 1.990710e-01
## ea_edapp_since_yng_br_0 7.662244e-02 0.097183401 1.423407e-01 3.744483e-03
## ea_edapp_since_yng_br_1 6.733665e-02 0.039780086 4.179857e-02 1.184464e-02
## ea_edapp_since_yng_br_2 2.695743e-02 0.005339515 9.038146e-02 6.015116e-06
## ea_edapp_since_yng_br_3 1.774833e-04 0.037479240 5.606863e-02 1.196812e-02
## ea_edapp_since_yng_br_4 5.646722e-03 0.291798132 6.986434e-02 7.186175e-03
## ea_edapp_since_yng_br_5 1.976022e-01 0.002482742 6.428119e-04 1.726609e-01
## ea_edapp_since_yng_br_6 1.517199e-01 0.082939964 2.948033e-03 1.749139e-01
## Dim 5
## ea_read_b4_ALL_br_0 2.554196e-02
## ea_read_b4_ALL_br_1 9.933979e-02
## ea_read_b4_ALL_br_2 2.655330e-02
## ea_read_b4_ALL_br_3 2.968448e-02
## ea_read_b4_ALL_br_4 1.053872e-02
## ea_read_b4_ALL_br_5 4.192566e-03
## ea_read_b4_ALL_br_6 5.833305e-04
## ea_indr_b4_ALL_br_0 6.018505e-02
## ea_indr_b4_ALL_br_1 1.317351e-01
## ea_indr_b4_ALL_br_2 5.186666e-02
## ea_indr_b4_ALL_br_3 2.622588e-02
## ea_indr_b4_ALL_br_4 4.844892e-02
## ea_indr_b4_ALL_br_5 2.864744e-04
## ea_indr_b4_ALL_br_6 1.823442e-03
## ea_letter_b4_yng_br_0 5.117816e-02
## ea_letter_b4_yng_br_1 1.706299e-01
## ea_letter_b4_yng_br_2 9.043694e-02
## ea_letter_b4_yng_br_3 1.544694e-02
## ea_letter_b4_yng_br_4 5.516772e-02
## ea_letter_b4_yng_br_5 3.644323e-03
## ea_letter_b4_yng_br_6 1.191069e-03
## ea_tv_b4_yng_br_0 7.489231e-02
## ea_tv_b4_yng_br_1 1.558024e-01
## ea_tv_b4_yng_br_2 5.894186e-02
## ea_tv_b4_yng_br_3 4.654270e-02
## ea_tv_b4_yng_br_4 6.100750e-02
## ea_tv_b4_yng_br_5 1.786510e-04
## ea_tv_b4_yng_br_6 4.327001e-03
## ea_boardg_b4_yng_br_0 4.333093e-02
## ea_boardg_b4_yng_br_1 9.259437e-02
## ea_boardg_b4_yng_br_2 4.863315e-02
## ea_boardg_b4_yng_br_3 3.615485e-04
## ea_boardg_b4_yng_br_4 3.217577e-02
## ea_boardg_b4_yng_br_5 7.647006e-04
## ea_boardg_b4_yng_br_6 3.884716e-03
## ea_vidg_b4_yng_br_0 8.790550e-02
## ea_vidg_b4_yng_br_1 1.512148e-01
## ea_vidg_b4_yng_br_2 6.103377e-02
## ea_vidg_b4_yng_br_3 3.275863e-02
## ea_vidg_b4_yng_br_4 6.816939e-02
## ea_vidg_b4_yng_br_5 1.042800e-03
## ea_vidg_b4_yng_br_6 5.379508e-03
## ea_edapp_b4_yng_br_0 4.851982e-02
## ea_edapp_b4_yng_br_1 1.144791e-01
## ea_edapp_b4_yng_br_2 5.003506e-02
## ea_edapp_b4_yng_br_3 3.389777e-02
## ea_edapp_b4_yng_br_4 5.065543e-02
## ea_edapp_b4_yng_br_5 5.209899e-06
## ea_edapp_b4_yng_br_6 1.159963e-02
## ea_read_since_yng_br_0 1.689168e-02
## ea_read_since_yng_br_1 1.052136e-01
## ea_read_since_yng_br_2 1.941730e-02
## ea_read_since_yng_br_3 4.887735e-02
## ea_read_since_yng_br_4 2.408557e-02
## ea_read_since_yng_br_5 1.561988e-02
## ea_read_since_yng_br_6 2.713641e-04
## ea_indr_since_yng_br_0 7.157578e-02
## ea_indr_since_yng_br_1 1.327915e-01
## ea_indr_since_yng_br_2 2.993181e-02
## ea_indr_since_yng_br_3 2.884344e-02
## ea_indr_since_yng_br_4 3.894863e-02
## ea_indr_since_yng_br_5 5.535271e-03
## ea_indr_since_yng_br_6 5.417847e-05
## ea_letter_since_yng_br_0 4.048348e-02
## ea_letter_since_yng_br_1 1.609562e-01
## ea_letter_since_yng_br_2 4.624361e-02
## ea_letter_since_yng_br_3 7.870264e-02
## ea_letter_since_yng_br_4 7.264988e-02
## ea_letter_since_yng_br_5 9.837216e-03
## ea_letter_since_yng_br_6 2.499999e-03
## ea_tv_since_yng_br_0 7.370269e-02
## ea_tv_since_yng_br_1 2.010822e-01
## ea_tv_since_yng_br_2 2.347738e-02
## ea_tv_since_yng_br_3 1.082001e-01
## ea_tv_since_yng_br_4 4.665818e-02
## ea_tv_since_yng_br_5 1.018597e-02
## ea_tv_since_yng_br_6 4.369671e-03
## ea_boardg_since_yng_br_0 3.791267e-02
## ea_boardg_since_yng_br_1 1.551907e-01
## ea_boardg_since_yng_br_2 5.916134e-02
## ea_boardg_since_yng_br_3 1.833455e-02
## ea_boardg_since_yng_br_4 3.179234e-02
## ea_boardg_since_yng_br_5 1.386309e-02
## ea_boardg_since_yng_br_6 5.053634e-03
## ea_vidg_since_yng_br_0 7.229416e-02
## ea_vidg_since_yng_br_1 1.634316e-01
## ea_vidg_since_yng_br_2 2.182915e-02
## ea_vidg_since_yng_br_3 9.138733e-02
## ea_vidg_since_yng_br_4 4.238376e-02
## ea_vidg_since_yng_br_5 8.465382e-03
## ea_vidg_since_yng_br_6 4.870444e-03
## ea_edapp_since_yng_br_0 4.534375e-02
## ea_edapp_since_yng_br_1 1.420503e-01
## ea_edapp_since_yng_br_2 1.726112e-02
## ea_edapp_since_yng_br_3 7.743289e-02
## ea_edapp_since_yng_br_4 3.331871e-02
## ea_edapp_since_yng_br_5 3.410453e-03
## ea_edapp_since_yng_br_6 6.274665e-03var_3f$contrib## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 0.3125225639 1.21690347 1.287385e+00 8.022837e-06
## ea_read_b4_ALL_br_1 0.4650481803 0.28613426 5.217078e-01 3.255968e-03
## ea_read_b4_ALL_br_2 0.1141075992 0.63833979 6.053912e-01 2.834249e-02
## ea_read_b4_ALL_br_3 0.0077747995 0.18787926 3.710586e-01 3.202474e-03
## ea_read_b4_ALL_br_4 0.2070207612 1.49132734 1.294501e+00 1.016841e-02
## ea_read_b4_ALL_br_5 3.4516980148 0.22554432 3.249836e-02 8.634022e-01
## ea_read_b4_ALL_br_6 0.6947015441 0.55518730 9.183854e-02 1.554060e+00
## ea_indr_b4_ALL_br_0 0.3223226157 1.01901716 2.659982e+00 4.053437e-02
## ea_indr_b4_ALL_br_1 0.8865081103 0.34340141 1.359663e+00 1.967701e-01
## ea_indr_b4_ALL_br_2 0.1506244068 0.23069247 5.842390e-01 4.812663e-02
## ea_indr_b4_ALL_br_3 0.0270126965 0.69583455 2.109274e-01 2.781048e-02
## ea_indr_b4_ALL_br_4 0.5862120901 1.35791737 1.497722e+00 1.170306e-02
## ea_indr_b4_ALL_br_5 2.6237076758 0.26148003 1.675685e-01 1.117857e-01
## ea_indr_b4_ALL_br_6 0.7617962433 0.47220718 6.835541e-02 5.278082e-01
## ea_letter_b4_yng_br_0 0.6791651963 1.06141036 2.352662e+00 1.038614e-02
## ea_letter_b4_yng_br_1 0.5669510720 0.30944122 1.184420e+00 5.967975e-02
## ea_letter_b4_yng_br_2 0.0010163917 1.30142993 1.253492e+00 2.227109e-01
## ea_letter_b4_yng_br_3 0.0734279398 1.06973984 2.366945e-02 3.825561e-03
## ea_letter_b4_yng_br_4 1.2671950701 1.58957595 1.528388e+00 5.315961e-02
## ea_letter_b4_yng_br_5 5.1052097651 1.16160593 2.457900e-01 1.452848e+00
## ea_letter_b4_yng_br_6 0.6999058701 0.26659783 2.275472e-01 1.533506e+00
## ea_tv_b4_yng_br_0 0.9718540374 1.55119994 2.672173e+00 1.105356e-01
## ea_tv_b4_yng_br_1 0.9490836301 0.55317020 1.157156e+00 3.147598e-01
## ea_tv_b4_yng_br_2 0.2315447740 0.63003586 1.520773e+00 9.462038e-02
## ea_tv_b4_yng_br_3 0.0069142060 0.73047531 2.927523e-01 4.397479e-01
## ea_tv_b4_yng_br_4 0.6429260920 2.51837419 1.930446e+00 8.976598e-02
## ea_tv_b4_yng_br_5 4.3498409924 0.50986148 1.166874e-01 3.740883e+00
## ea_tv_b4_yng_br_6 1.1164702686 1.05868441 7.015491e-05 5.484305e+00
## ea_boardg_b4_yng_br_0 0.3066957487 1.92089187 2.049793e+00 5.258316e-02
## ea_boardg_b4_yng_br_1 0.6035582997 0.09128605 5.095425e-01 3.215549e-01
## ea_boardg_b4_yng_br_2 0.0263871430 0.98501657 1.043175e+00 2.961499e-02
## ea_boardg_b4_yng_br_3 0.3253568811 1.37517809 1.285429e-02 3.815981e-02
## ea_boardg_b4_yng_br_4 0.3745838550 1.76511560 1.120427e+00 1.243350e-01
## ea_boardg_b4_yng_br_5 4.1537997877 0.73217430 9.070950e-02 2.096548e+00
## ea_boardg_b4_yng_br_6 1.6099851496 0.58768102 1.933029e-01 2.896633e+00
## ea_vidg_b4_yng_br_0 0.7129849608 1.22237589 2.465907e+00 1.354257e-01
## ea_vidg_b4_yng_br_1 1.0620106085 0.60129137 1.386975e+00 1.521062e-01
## ea_vidg_b4_yng_br_2 0.2064804562 0.77419504 1.634107e+00 1.642334e-02
## ea_vidg_b4_yng_br_3 0.1226508543 0.65897595 7.421022e-02 3.030222e-01
## ea_vidg_b4_yng_br_4 0.3194096054 2.19096188 2.743067e+00 1.818936e-02
## ea_vidg_b4_yng_br_5 3.9595537705 0.21612605 1.232301e-01 3.983807e+00
## ea_vidg_b4_yng_br_6 1.3743863667 1.57700925 1.416017e-05 7.416914e+00
## ea_edapp_b4_yng_br_0 0.4499495846 0.96589408 1.937041e+00 7.625499e-02
## ea_edapp_b4_yng_br_1 1.0345849279 0.43226468 8.666792e-01 2.043539e-01
## ea_edapp_b4_yng_br_2 0.1447735988 0.42595687 2.080919e+00 1.345231e-03
## ea_edapp_b4_yng_br_3 0.0231088271 0.51581238 1.799661e-01 4.910944e-01
## ea_edapp_b4_yng_br_4 0.3593045803 3.05788286 2.169499e+00 1.512988e-04
## ea_edapp_b4_yng_br_5 3.0231459009 0.20982725 7.691197e-03 3.104496e+00
## ea_edapp_b4_yng_br_6 1.8706444420 1.54857548 5.618428e-02 5.207868e+00
## ea_read_since_yng_br_0 0.4653308809 0.93548290 1.285629e+00 7.647279e-03
## ea_read_since_yng_br_1 0.7587677384 0.20741656 2.507562e-01 2.759592e-01
## ea_read_since_yng_br_2 0.1479028845 0.48149828 1.022011e+00 3.102532e-02
## ea_read_since_yng_br_3 0.0148823063 0.37819914 6.820066e-01 1.117859e-01
## ea_read_since_yng_br_4 0.4048948078 1.90483380 1.635017e+00 1.544575e-01
## ea_read_since_yng_br_5 2.2111642875 0.08291405 4.594767e-02 2.834751e+00
## ea_read_since_yng_br_6 2.5371037147 1.66957199 4.188508e-01 2.174916e+00
## ea_indr_since_yng_br_0 0.5660434867 1.15958866 2.232250e+00 5.664969e-02
## ea_indr_since_yng_br_1 0.7047208303 0.43177434 5.906861e-01 2.776249e-01
## ea_indr_since_yng_br_2 0.1726905443 0.74692850 1.342512e+00 4.157992e-02
## ea_indr_since_yng_br_3 0.0207241874 0.52496680 3.717937e-01 3.910110e-02
## ea_indr_since_yng_br_4 0.4499008540 1.81109551 1.392576e+00 6.906432e-08
## ea_indr_since_yng_br_5 1.9811416315 0.20272044 6.516311e-03 1.533629e+00
## ea_indr_since_yng_br_6 1.2849052222 0.87081720 2.814625e-01 2.182906e+00
## ea_letter_since_yng_br_0 0.7779788350 1.50136018 2.933465e+00 2.736020e-02
## ea_letter_since_yng_br_1 0.6869013525 0.18516834 1.326077e+00 3.028558e-01
## ea_letter_since_yng_br_2 0.0006143455 0.96428047 1.672750e+00 1.414106e-02
## ea_letter_since_yng_br_3 0.0390393464 0.89577274 7.428687e-01 1.985428e-01
## ea_letter_since_yng_br_4 1.2040760563 2.53633765 2.163592e+00 3.493474e-02
## ea_letter_since_yng_br_5 2.8245450224 0.47804150 5.402160e-02 3.780016e+00
## ea_letter_since_yng_br_6 2.8259430480 1.50774731 4.401818e-01 2.352944e+00
## ea_tv_since_yng_br_0 1.0625304361 2.12951107 3.349580e+00 1.515612e-02
## ea_tv_since_yng_br_1 1.1806010319 0.67740199 6.691839e-01 6.994835e-01
## ea_tv_since_yng_br_2 0.3822584959 0.24924205 1.749035e+00 1.056440e-03
## ea_tv_since_yng_br_3 0.0313621998 0.68457399 1.591642e+00 3.859352e-01
## ea_tv_since_yng_br_4 0.3241241405 3.59085005 2.233134e+00 1.849320e-01
## ea_tv_since_yng_br_5 3.2896705557 0.06083044 4.456949e-04 5.334042e+00
## ea_tv_since_yng_br_6 2.4562618291 2.16350296 6.983332e-02 4.976127e+00
## ea_boardg_since_yng_br_0 0.4229538359 1.45827301 3.212861e+00 1.770699e-02
## ea_boardg_since_yng_br_1 0.6773360718 0.50833361 3.833774e-01 1.087819e-01
## ea_boardg_since_yng_br_2 0.1644301885 0.74681947 1.447192e+00 9.851753e-03
## ea_boardg_since_yng_br_3 0.0964303206 1.32027062 1.182466e-01 2.576176e-01
## ea_boardg_since_yng_br_4 0.7313212568 2.56516372 1.731397e+00 2.305471e-02
## ea_boardg_since_yng_br_5 3.9544649885 0.58228948 1.512370e-01 4.935915e+00
## ea_boardg_since_yng_br_6 1.9981776284 1.48585147 3.999708e-01 4.980758e+00
## ea_vidg_since_yng_br_0 0.9402658381 1.92603506 2.778224e+00 7.555334e-02
## ea_vidg_since_yng_br_1 1.1274436028 0.87385316 5.276424e-01 4.026494e-01
## ea_vidg_since_yng_br_2 0.2994406887 0.22328845 2.142288e+00 2.962167e-03
## ea_vidg_since_yng_br_3 0.0331529287 0.60577887 1.000879e+00 2.067877e-01
## ea_vidg_since_yng_br_4 0.0896735221 3.60521927 2.260616e+00 2.179087e-01
## ea_vidg_since_yng_br_5 2.6319724844 0.09009768 2.661972e-03 4.193770e+00
## ea_vidg_since_yng_br_6 1.8945929378 1.70913144 2.889754e-02 4.384971e+00
## ea_edapp_since_yng_br_0 0.8567383880 1.31699941 2.368551e+00 6.930128e-02
## ea_edapp_since_yng_br_1 0.8301328317 0.59437845 7.668648e-01 2.416990e-01
## ea_edapp_since_yng_br_2 0.3471471895 0.08333706 1.732111e+00 1.282142e-04
## ea_edapp_since_yng_br_3 0.0023449258 0.60015515 1.102434e+00 2.617307e-01
## ea_edapp_since_yng_br_4 0.0659707787 4.13179722 1.214709e+00 1.389665e-01
## ea_edapp_since_yng_br_5 2.6532268511 0.04040317 1.284482e-02 3.837371e+00
## ea_edapp_since_yng_br_6 2.0407847145 1.35213443 5.901311e-02 3.894362e+00
## Dim 5
## ea_read_b4_ALL_br_0 0.5505104647
## ea_read_b4_ALL_br_1 1.8510603347
## ea_read_b4_ALL_br_2 0.5802883185
## ea_read_b4_ALL_br_3 0.7188133451
## ea_read_b4_ALL_br_4 0.2266904807
## ea_read_b4_ALL_br_5 0.1036835869
## ea_read_b4_ALL_br_6 0.0151272235
## ea_indr_b4_ALL_br_0 1.2945951622
## ea_indr_b4_ALL_br_1 2.5565106083
## ea_indr_b4_ALL_br_2 1.1134375187
## ea_indr_b4_ALL_br_3 0.6204250247
## ea_indr_b4_ALL_br_4 1.0525486881
## ea_indr_b4_ALL_br_5 0.0071461088
## ea_indr_b4_ALL_br_6 0.0468949747
## ea_letter_b4_yng_br_0 0.9162802046
## ea_letter_b4_yng_br_1 3.1281721964
## ea_letter_b4_yng_br_2 1.9492034130
## ea_letter_b4_yng_br_3 0.3839976631
## ea_letter_b4_yng_br_4 1.3216816272
## ea_letter_b4_yng_br_5 0.0934111661
## ea_letter_b4_yng_br_6 0.0311942262
## ea_tv_b4_yng_br_0 1.5884434233
## ea_tv_b4_yng_br_1 3.1974930212
## ea_tv_b4_yng_br_2 1.2374859376
## ea_tv_b4_yng_br_3 1.1190440949
## ea_tv_b4_yng_br_4 1.3201442325
## ea_tv_b4_yng_br_5 0.0043950889
## ea_tv_b4_yng_br_6 0.1099806642
## ea_boardg_b4_yng_br_0 0.8502012967
## ea_boardg_b4_yng_br_1 1.6180300837
## ea_boardg_b4_yng_br_2 1.0022619635
## ea_boardg_b4_yng_br_3 0.0086928511
## ea_boardg_b4_yng_br_4 0.8053878836
## ea_boardg_b4_yng_br_5 0.0196664455
## ea_boardg_b4_yng_br_6 0.1007404374
## ea_vidg_b4_yng_br_0 1.8870945343
## ea_vidg_b4_yng_br_1 3.0189423311
## ea_vidg_b4_yng_br_2 1.2787852667
## ea_vidg_b4_yng_br_3 0.7805960082
## ea_vidg_b4_yng_br_4 1.5160963306
## ea_vidg_b4_yng_br_5 0.0257888016
## ea_vidg_b4_yng_br_6 0.1358086690
## ea_edapp_b4_yng_br_0 0.9561796105
## ea_edapp_b4_yng_br_1 2.3740009084
## ea_edapp_b4_yng_br_2 1.0848592223
## ea_edapp_b4_yng_br_3 0.7946416666
## ea_edapp_b4_yng_br_4 1.1483322677
## ea_edapp_b4_yng_br_5 0.0001304084
## ea_edapp_b4_yng_br_6 0.2918431706
## ea_read_since_yng_br_0 0.3350599668
## ea_read_since_yng_br_1 2.0734425800
## ea_read_since_yng_br_2 0.4368453755
## ea_read_since_yng_br_3 1.1562902179
## ea_read_since_yng_br_4 0.5346321614
## ea_read_since_yng_br_5 0.3909792624
## ea_read_since_yng_br_6 0.0069206436
## ea_indr_since_yng_br_0 1.5181009332
## ea_indr_since_yng_br_1 2.5656087574
## ea_indr_since_yng_br_2 0.6798235822
## ea_indr_since_yng_br_3 0.6811101495
## ea_indr_since_yng_br_4 0.8411390284
## ea_indr_since_yng_br_5 0.1376020932
## ea_indr_since_yng_br_6 0.0013607872
## ea_letter_since_yng_br_0 0.7109004629
## ea_letter_since_yng_br_1 3.1650517848
## ea_letter_since_yng_br_2 1.0264784456
## ea_letter_since_yng_br_3 1.8922779139
## ea_letter_since_yng_br_4 1.6968420377
## ea_letter_since_yng_br_5 0.2538366077
## ea_letter_since_yng_br_6 0.0645092422
## ea_tv_since_yng_br_0 1.6613082365
## ea_tv_since_yng_br_1 4.2648923150
## ea_tv_since_yng_br_2 0.5221400973
## ea_tv_since_yng_br_3 2.4992960782
## ea_tv_since_yng_br_4 0.9515442888
## ea_tv_since_yng_br_5 0.2501532204
## ea_tv_since_yng_br_6 0.1065625599
## ea_boardg_since_yng_br_0 0.7959775483
## ea_boardg_since_yng_br_1 2.8451236174
## ea_boardg_since_yng_br_2 1.1963726598
## ea_boardg_since_yng_br_3 0.4329527050
## ea_boardg_since_yng_br_4 0.7630306039
## ea_boardg_since_yng_br_5 0.3541479709
## ea_boardg_since_yng_br_6 0.1299685423
## ea_vidg_since_yng_br_0 1.6916375025
## ea_vidg_since_yng_br_1 3.5084365331
## ea_vidg_since_yng_br_2 0.4836087139
## ea_vidg_since_yng_br_3 2.1266346293
## ea_vidg_since_yng_br_4 0.8625523166
## ea_vidg_since_yng_br_5 0.2020826176
## ea_vidg_since_yng_br_6 0.1164746352
## ea_edapp_since_yng_br_0 0.9111101204
## ea_edapp_since_yng_br_1 3.1470205340
## ea_edapp_since_yng_br_2 0.3994525669
## ea_edapp_since_yng_br_3 1.8384770155
## ea_edapp_since_yng_br_4 0.6995272942
## ea_edapp_since_yng_br_5 0.0822916949
## ea_edapp_since_yng_br_6 0.1516723928#Correlação entre variáveis e dimensões principais correlação entre as variáveis e as dimensões principais do MCA
Pode-se observar que para as duas principais dimensões, as variáveis são homogeneamente bem distribuídas e explicadas pelas duas componentes.
fviz_mca_var(res.mca_3f, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
head(round(var_3f$coord, 2), 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 -0.34 0.61 -0.57 0.00 0.34
## ea_read_b4_ALL_br_1 -0.33 0.23 0.28 -0.02 -0.49
## ea_read_b4_ALL_br_2 -0.21 -0.46 0.40 0.08 0.36
## ea_read_b4_ALL_br_3 -0.08 -0.36 0.45 -0.04 0.57para visualizar apenas categorias de variáveis: Agora visualizando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionário, é possível notas uma distribuição homogênea em torno das duas componentes, com isso as alternativas são bem explicadas por elas.
fviz_mca_var(res.mca_3f,
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
head(var_3f$cos2, 10)## Dim 1 Dim 2 Dim 3 Dim 4
## ea_read_b4_ALL_br_0 0.0260574124 0.08371529 0.072126880 4.041297e-07
## ea_read_b4_ALL_br_1 0.0448499379 0.02276840 0.033808810 1.897086e-04
## ea_read_b4_ALL_br_2 0.0093831879 0.04330983 0.033451113 1.408046e-03
## ea_read_b4_ALL_br_3 0.0005769839 0.01150409 0.018503578 1.435830e-04
## ea_read_b4_ALL_br_4 0.0172953678 0.10279870 0.072670271 5.132296e-04
## ea_read_b4_ALL_br_5 0.2508210231 0.01352265 0.001586833 3.790419e-02
## ea_read_b4_ALL_br_6 0.0481409827 0.03174351 0.004276419 6.506199e-02
## ea_indr_b4_ALL_br_0 0.0269281600 0.07024188 0.149325242 2.045890e-03
## ea_indr_b4_ALL_br_1 0.0820914457 0.02623711 0.084602895 1.100822e-02
## ea_indr_b4_ALL_br_2 0.0126089514 0.01593367 0.032863427 2.433952e-03
## Dim 5
## ea_read_b4_ALL_br_0 0.0255419626
## ea_read_b4_ALL_br_1 0.0993397923
## ea_read_b4_ALL_br_2 0.0265532967
## ea_read_b4_ALL_br_3 0.0296844789
## ea_read_b4_ALL_br_4 0.0105387223
## ea_read_b4_ALL_br_5 0.0041925662
## ea_read_b4_ALL_br_6 0.0005833305
## ea_indr_b4_ALL_br_0 0.0601850542
## ea_indr_b4_ALL_br_1 0.1317351186
## ea_indr_b4_ALL_br_2 0.0518666561Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados.
fviz_mca_var(res.mca_3f, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_cos2(res.mca_3f, choice = "var", axes = 1:2)
head(round(var_3f$contrib,2), 49)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 0.31 1.22 1.29 0.00 0.55
## ea_read_b4_ALL_br_1 0.47 0.29 0.52 0.00 1.85
## ea_read_b4_ALL_br_2 0.11 0.64 0.61 0.03 0.58
## ea_read_b4_ALL_br_3 0.01 0.19 0.37 0.00 0.72
## ea_read_b4_ALL_br_4 0.21 1.49 1.29 0.01 0.23
## ea_read_b4_ALL_br_5 3.45 0.23 0.03 0.86 0.10
## ea_read_b4_ALL_br_6 0.69 0.56 0.09 1.55 0.02
## ea_indr_b4_ALL_br_0 0.32 1.02 2.66 0.04 1.29
## ea_indr_b4_ALL_br_1 0.89 0.34 1.36 0.20 2.56
## ea_indr_b4_ALL_br_2 0.15 0.23 0.58 0.05 1.11
## ea_indr_b4_ALL_br_3 0.03 0.70 0.21 0.03 0.62
## ea_indr_b4_ALL_br_4 0.59 1.36 1.50 0.01 1.05
## ea_indr_b4_ALL_br_5 2.62 0.26 0.17 0.11 0.01
## ea_indr_b4_ALL_br_6 0.76 0.47 0.07 0.53 0.05
## ea_letter_b4_yng_br_0 0.68 1.06 2.35 0.01 0.92
## ea_letter_b4_yng_br_1 0.57 0.31 1.18 0.06 3.13
## ea_letter_b4_yng_br_2 0.00 1.30 1.25 0.22 1.95
## ea_letter_b4_yng_br_3 0.07 1.07 0.02 0.00 0.38
## ea_letter_b4_yng_br_4 1.27 1.59 1.53 0.05 1.32
## ea_letter_b4_yng_br_5 5.11 1.16 0.25 1.45 0.09
## ea_letter_b4_yng_br_6 0.70 0.27 0.23 1.53 0.03
## ea_tv_b4_yng_br_0 0.97 1.55 2.67 0.11 1.59
## ea_tv_b4_yng_br_1 0.95 0.55 1.16 0.31 3.20
## ea_tv_b4_yng_br_2 0.23 0.63 1.52 0.09 1.24
## ea_tv_b4_yng_br_3 0.01 0.73 0.29 0.44 1.12
## ea_tv_b4_yng_br_4 0.64 2.52 1.93 0.09 1.32
## ea_tv_b4_yng_br_5 4.35 0.51 0.12 3.74 0.00
## ea_tv_b4_yng_br_6 1.12 1.06 0.00 5.48 0.11
## ea_boardg_b4_yng_br_0 0.31 1.92 2.05 0.05 0.85
## ea_boardg_b4_yng_br_1 0.60 0.09 0.51 0.32 1.62
## ea_boardg_b4_yng_br_2 0.03 0.99 1.04 0.03 1.00
## ea_boardg_b4_yng_br_3 0.33 1.38 0.01 0.04 0.01
## ea_boardg_b4_yng_br_4 0.37 1.77 1.12 0.12 0.81
## ea_boardg_b4_yng_br_5 4.15 0.73 0.09 2.10 0.02
## ea_boardg_b4_yng_br_6 1.61 0.59 0.19 2.90 0.10
## ea_vidg_b4_yng_br_0 0.71 1.22 2.47 0.14 1.89
## ea_vidg_b4_yng_br_1 1.06 0.60 1.39 0.15 3.02
## ea_vidg_b4_yng_br_2 0.21 0.77 1.63 0.02 1.28
## ea_vidg_b4_yng_br_3 0.12 0.66 0.07 0.30 0.78
## ea_vidg_b4_yng_br_4 0.32 2.19 2.74 0.02 1.52
## ea_vidg_b4_yng_br_5 3.96 0.22 0.12 3.98 0.03
## ea_vidg_b4_yng_br_6 1.37 1.58 0.00 7.42 0.14
## ea_edapp_b4_yng_br_0 0.45 0.97 1.94 0.08 0.96
## ea_edapp_b4_yng_br_1 1.03 0.43 0.87 0.20 2.37
## ea_edapp_b4_yng_br_2 0.14 0.43 2.08 0.00 1.08
## ea_edapp_b4_yng_br_3 0.02 0.52 0.18 0.49 0.79
## ea_edapp_b4_yng_br_4 0.36 3.06 2.17 0.00 1.15
## ea_edapp_b4_yng_br_5 3.02 0.21 0.01 3.10 0.00
## ea_edapp_b4_yng_br_6 1.87 1.55 0.06 5.21 0.2915 principais categorias de variáveis que contribuem para as dimensões:
# Contributions of rows to dimension 1
fviz_contrib(res.mca_3f, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.mca_3f, choice = "var", axes = 2, top = 15)
fviz_contrib(res.mca_3f, choice = "var", axes = 1:2, top = 15)
fviz_mca_var(res.mca_3f, alpha.var="contrib",
repel = TRUE,
ggtheme = theme_minimal())## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
##Gráfico de indivíduos Resultados
lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:
ind_3f <- get_mca_ind(res.mca_3f)
ind_3f## Multiple Correspondence Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"colorir os indivíduos por seus valores de cos2:
fviz_mca_ind(res.mca_3f, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping (slow if many points)
ggtheme = theme_minimal())## Warning: ggrepel: 525 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Cos2 of individuals
fviz_cos2(res.mca_3f, choice = "ind", axes = 1:2, top = 20)
# Contribution of individuals to the dimensions
fviz_contrib(res.mca_3f, choice = "ind", axes = 1:2, top = 20)
PCA COMO NUMERICO
PRE-PANDEMIA
Com o pacote “psych” Como o p-valor e praticamente 0, a matrix de correlacao nao e diagonal, ou seja, as variaveis seao correlacionadas.
require(psych)
cortest.bartlett(db_bloco_3_pre)## $chisq
## [1] 1612.427
##
## $p.value
## [1] 0
##
## $df
## [1] 21The test measures sampling adequacy for each variable in the model and for the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. The lower the proportion, the more suited your data is to Factor Analysis.
KMO(db_bloco_3_pre)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = db_bloco_3_pre)
## Overall MSA = 0.82
## MSA for each item =
## ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br ea_tv_b4_yng_br
## 0.83 0.85 0.81 0.84
## ea_boardg_b4_yng_br ea_vidg_b4_yng_br ea_edapp_b4_yng_br
## 0.80 0.79 0.82res.pca_3_pre <- PCA(db_bloco_3_pre)
res.pca_3_pre$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 3.4809424 49.727749 49.72775
## comp 2 1.1398488 16.283554 66.01130
## comp 3 0.7329582 10.470831 76.48213
## comp 4 0.6073807 8.676867 85.15900
## comp 5 0.3908762 5.583946 90.74295
## comp 6 0.3638460 5.197800 95.94075
## comp 7 0.2841477 4.059252 100.00000PCA_3_pre <- prcomp(db_bloco_3_pre, scale = TRUE)
print(PCA_3_pre)## Standard deviations (1, .., p=7):
## [1] 1.8657284 1.0676370 0.8561298 0.7793463 0.6252010 0.6031965 0.5330550
##
## Rotation (n x k) = (7 x 7):
## PC1 PC2 PC3 PC4 PC5
## ea_read_b4_ALL_br 0.2826357 -0.4830310 -0.64011136 0.47003914 0.20002989
## ea_indr_b4_ALL_br 0.3285644 -0.4135537 -0.18772773 -0.79496576 -0.05989485
## ea_letter_b4_yng_br 0.3967780 -0.2600564 0.40839105 0.35503281 -0.51659110
## ea_tv_b4_yng_br 0.4336358 0.2763527 -0.13630629 -0.02636210 -0.51375515
## ea_boardg_b4_yng_br 0.3836164 -0.2484833 0.58651398 0.01499558 0.53886532
## ea_vidg_b4_yng_br 0.4156935 0.4129075 -0.07472999 -0.10468235 0.12411495
## ea_edapp_b4_yng_br 0.3830498 0.4684204 -0.14166817 0.09573986 0.34613519
## PC6 PC7
## ea_read_b4_ALL_br -0.1252620 0.02040298
## ea_indr_b4_ALL_br 0.2218518 0.03163696
## ea_letter_b4_yng_br 0.3650744 0.28628771
## ea_tv_b4_yng_br -0.3982876 -0.54197550
## ea_boardg_b4_yng_br -0.2988610 -0.25918579
## ea_vidg_b4_yng_br -0.3517244 0.70784800
## ea_edapp_b4_yng_br 0.6558589 -0.23379194summary(PCA_3_pre)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.8657 1.0676 0.8561 0.77935 0.62520 0.60320 0.53306
## Proportion of Variance 0.4973 0.1628 0.1047 0.08677 0.05584 0.05198 0.04059
## Cumulative Proportion 0.4973 0.6601 0.7648 0.85159 0.90743 0.95941 1.00000screeplot(PCA_3_pre, type = c("lines"), main = deparse(substitute(PCA)))
biplot(princomp(db_bloco_3_pre, cor = T))
fviz_pca_ind(res.pca_3_pre,
col.ind = "cos2", # Color by the quality of representation
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)## Warning: ggrepel: 540 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_pca_var(res.pca_3_pre,
col.var = "contrib", # Color by contributions to the PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
fviz_pca_biplot(res.pca_3_pre, repel = TRUE,
col.var = "#2E9FDF", # Variables color
col.ind = "#696969" # Individuals color
)## Warning: ggrepel: 528 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps